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A Novel Method for Multiple Biomedical Events Extraction with Reinforcement learning and Knowledge Bases

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A Novel Method for Multiple Biomedical Events Extraction wit...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Weizhong Zhao Yao Zhao Xingpeng Jiang Tingting He Fan Liu Ning Li Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan Hubei PR China School of Computer Central China Normal University Wuhan Hubei PR China Central China Normal University Wuhan Hubei PR China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Comcast Silicon Valley Innovation Center Sunnyvale CA USA Chinese Academy of Sciences Institute of Information Engineering Beijing China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Biomedical event extraction is usually modeled as two sub-tasks: trigger identification and argument detection. Most existing methods perform these two sub-tasks sequentially but ignore the interaction between them. This paper proposes a novel method for multiple biomedical events extraction, in which the task of event extraction is modeled under a framework of reinforcement learning (RL). We treat the trigger identification and argument detection as main-task and subsidiary-task, respectively. And the result of argument detection is modeled as environmental information. In this way, the proposed method can capture the interaction between two sub-tasks, and the semantic associations among multiple biomedical events are also utilized effectively. Moreover, external biomedical knowledge bases are employed for representation learning of biomedical text. Comprehensive experiments are conducted on two widely used biomedical corpora, and results demonstrate that our method gains better performance compared to existing methods, especially in multiple biomedical events extraction.

关键词： Task analysis Biological system modeling Ontologies Semantics Data mining Feature extraction Reinforcement learning

artificial intelligence Development Races in Heterogeneous Settings

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arXiv 2020年

作者： Cimpeanu, Theodor Santos, Francisco C. Pereira, Luís Moniz Lenaerts, Tom Han, The Anh School of Computing Engineering and Digital Technologies Teesside University Middlesbrough TS1 3BA United Kingdom Faculdade de Ciências e Tecnologia Universidade Nova de Lisboa Caparica2829-516 Portugal INESC-ID Instituto Superior Técnico Universidade de Lisboa Portugal Machine Learning Group Université Libre de Bruxelles Brussels1050 Belgium Artificial Intelligence Lab Vrije Universiteit Brussel Brussels1050 Belgium Center for Human-Compatible AI UC Berkeley Berkeley94702 United States FARI Institute Université Libre de Bruxelles-Vrije Universiteit Brussel Brussels1050 Belgium

Regulation of advanced technologies such as artificial intelligence (AI) has become increasingly important, given the associated risks and apparent ethical issues. With the great benefits promised from being able to first supply such technologies, safety precautions and societal consequences might be ignored or shortchanged in exchange for speeding up the development, therefore engendering a racing narrative among the developers. Starting from a game-theoretical model describing an idealised technology race in a fully connected world of players, here we investigate how different interaction structures among race participants can alter collective choices and requirements for regulatory actions. Our findings indicate that, when participants portray a strong diversity in terms of connections and peer-influence (e.g., when scale-free networks shape interactions among parties), the conflicts that exist in homogeneous settings are significantly reduced, thereby lessening the need for regulatory actions. Furthermore, our results suggest that technology governance and regulation may profit from the world’s patent heterogeneity and inequality among firms and nations, so as to enable the design and implementation of meticulous interventions on a minority of participants, which is capable of influencing an entire population towards an ethical and sustainable use of advanced technologies. © 2020, CC BY.

关键词： Complex networks

Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D'Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Spyridon Ca Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United State

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Tumors

An Early Stage Researcher's Primer on Systems Medicine Terminology

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Network and Systems Medicine

Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

Deep weighted averaging classifiers

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arXiv 2018年

作者： Card, Dallas Zhang, Michael Smith, Noah A. Machine Learning Department Carnegie Mellon University PittsburghPA United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States Allen Institute for Artificial Intelligence SeattleWA United States

Recent advances in deep learning have achieved impressive gains in classification accuracy on a variety of types of data, including images and text. Despite these gains, however, concerns have been raised about the calibration, robustness, and interpretability of these models. In this paper we propose a simple way to modify any conventional deep architecture to automatically provide more transparent explanations for classification decisions, as well as an intuitive notion of the credibility of each prediction. Specifically, we draw on ideas from nonparametric kernel regression, and propose to predict labels based on a weighted sum of training instances, where the weights are determined by distance in a learned instanceembedding space. Working within the framework of conformal methods, we propose a new measure of nonconformity suggested by our model, and experimentally validate the accompanying theoretical expectations, demonstrating improved transparency, controlled error rates, and robustness to out-of-domain data, without compromising on accuracy or calibration. Copyright © 2018, The Authors. All rights reserved.

关键词： Calibration

Constraints on the photon polarisation in b → sγ transitions using Bs0→ ϕe⁺e⁻ decays

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Journal of High Energy Physics 2025年第3期2025卷 1-23页

作者： Aaij, R. Abdelmotteleb, A.S.W. Abellan Beteta, C. Abudinén, F. Ackernley, T. Adefisoye, A.A. Adeva, B. Adinolfi, M. Adlarson, P. Agapopoulou, C. Aidala, C.A. Ajaltouni, Z. Akar, S. Akiba, K. Albicocco, P. Albrecht, J. Alessio, F. Alexander, M. Aliouche, Z. Alvarez Cartelle, P. Amalric, R. Amato, S. Amey, J.L. Amhis, Y. An, L. Anderlini, L. Andersson, M. Andreianov, A. Andreola, P. Andreotti, M. Andreou, D. Anelli, A. Ao, D. Archilli, F. Argenton, M. Arguedas Cuendis, S. Artamonov, A. Artuso, M. Aslanides, E. Ataíde Da Silva, R. Atzeni, M. Audurier, B. Bacher, D. Bachiller Perea, I. Bachmann, S. Bachmayer, M. Back, J.J. Baladron Rodriguez, P. Balagura, V. Baldini, W. Balzani, L. Bao, H. Baptista de Souza Leite, J. Barbero Pretel, C. Barbetti, M. Barbosa, I.R. Barlow, R.J. Barnyakov, M. Barsuk, S. Barter, W. Bartolini, M. Bartz, J. Basels, J.M. Bashir, S. Bassi, G. Batsukh, B. Battista, P.B. Bay, A. Beck, A. Becker, M. Bedeschi, F. Bediaga, I.B. Behling, N.A. Belin, S. Bellee, V. Belous, K. Belov, I. Belyaev, I. Benane, G. Bencivenni, G. Ben-Haim, E. Berezhnoy, A. Bernet, R. Bernet Andres, S. Bertolin, A. Betancourt, C. Betti, F. Bex, J. Bezshyiko, Ia. Bhom, J. Bieker, M.S. Biesuz, N.V. Billoir, P. Biolchini, A. Birch, M. Bishop, F.C.R. Bitadze, A. Bizzeti, A. Blake, T. Blanc, F. Blank, J.E. Blusk, S. Bocharnikov, V. Boelhauve, J.A. Boente Garcia, O. Boettcher, T. Bohare, A. Boldyrev, A. Bolognani, C.S. Bolzonella, R. Bondar, N. Bordelius, A. Borgato, F. Borghi, S. Borsato, M. Borsuk, J.T. Bouchiba, S.A. Bovill, M. Bowcock, T.J.V. Boyer, A. Bozzi, C. Brea Rodriguez, A. Breer, N. Brodzicka, J. Brossa Gonzalo, A. Brown, J. Brundu, D. Buchanan, E. Buonaura, A. Buonincontri, L. Burke, A.T. Burr, C. Butter, J.S. Buytaert, J. Byczynski, W. Cadeddu, S. Cai, H. Caillet, A.C. Calabrese, R. Calderon Ramirez, S. Calefice, L. Cali, S. Calvi, M. Calvo Gomez, M. Camargo Magalhaes, P. Cambon Bouzas, J.I. Campana, P. Campora Perez, D.H. Campoverde Quezada, A.F. Capelli, S. Capriotti, L. Caravaca-Mora, R. Carbone, A. Carcedo Salgad School of Physics and Astronomy Monash University Melbourne Australia Centro Brasileiro de Pesquisas Físicas (CBPF) Rio de Janeiro Brazil Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Department of Engineering Physics Tsinghua University Beijing China Institute Of High Energy Physics (IHEP) Beijing China School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China University of Chinese Academy of Sciences Beijing China Institute of Particle Physics Central China Normal University Hubei Wuhan China Consejo Nacional de Rectores (CONARE) San Jose Costa Rica Université Savoie Mont Blanc CNRS IN2P3-LAPP Annecy France Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Université Paris-Saclay Centre d’Etudes de Saclay (CEA) IRFU Saclay France Gif-Sur-Yvette France Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France I. Physikalisches Institut RWTH Aachen University Aachen Germany Universität Bonn — Helmholtz-Institut für Strahlen und Kernphysik Bonn Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Physikalisches Institut Albert-Ludwigs-Universität Freiburg Freiburg Germany Max-Planck-Institut für Kernphysik (MPIK) Heidelberg Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany School of Physics University College Dublin Dublin Ireland INFN Sezione di Bari Bari Italy INFN Sezione di Bologna Bologna Italy INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Genova Genova Italy INFN Sezione di Milano Milano Italy INFN Sezione di Milano-Bicocca Milano Italy INFN

An angular analysis of the Bs0→ ϕe+e− decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of 9 fb−1 at centre... 详细信息

An angular analysis of the Bs0→ ϕe⁺e⁻ decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of 9 fb⁻¹ at centre-of-mass energies of 7, 8 and 13 TeV. The analysis is performed in the very low dielectron invariant mass-squared region between 0.0009 and 0.2615 GeV²/c⁴. The longitudinal polarisation fraction of the ϕ meson is measured to be less than 11.5% at 90% confidence level. The ATReCP observable, which is related to the lepton forward-backward asymmetry, is measured to be 0.116 ± 0.155 ± 0.006, where the first uncertainty is statistical and the second systematic. The transverse asymmetries, AT2 and ATImCP, which are sensitive to the virtual photon polarisation, are found to be −0.045 ± 0.235 ± 0.014 and 0.002 ± 0.247 ± 0.016, respectively. The results are consistent with Standard Model predictions. © The Author(s) 2025.

关键词： B Physics Flavour Changing Neutral Currents Hadron-Hadron Scattering Rare Decay

Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation

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arXiv 2024年

作者： LaBella, Dominic Schumacher, Katherine Mix, Michael Leu, Kevin McBurney-Lin, Shan Nedelec, Pierre Villanueva-Meyer, Javier Shapey, Jonathan Vercauteren, Tom Chia, Kazumi Ivory, Marina Barfoot, Theodore Al-Salihi, Omar Leu, Justin Halasz, Lia Velichko, Yury Wang, Chunhao Kirkpatrick, John Floyd, Scott Reitman, Zachary J. Mullikin, Trey Bagci, Ulas Sachdev, Sean Hattangadi-Gluth, Jona A. Seibert, Tyler M. Farid, Nikdokht Puett, Connor Pease, Matthew W. Shiue, Kevin Anwar, Syed Muhammad Faghani, Shahriar Taylor, Peter Haider, Muhammad Ammar Warman, Pranav Albrecht, Jake Jakab, András Moassefi, Mana Chung, Verena Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Coley, Aaron Huang, Christina Ghanta, Siddharth Schneider, Alex Sharp, Conrad Saluja, Rachit Kofler, Florian Lohmann, Philipp Vollmuth, Phillipp Gagnon, Louis Adewole, Maruf Li, Hongwei Bran Kazerooni, Anahita Fathi Tahon, Nourel Hoda Anazodo, Udunna Moawad, Ahmed W. Menze, Bjoern Linguraru, Marius George Aboian, Mariam Wiestler, Benedikt Baid, Ujjwal Conte, Gian-Marco Rauschecker, Andreas M. Nada, Ayman Abayazeed, Aly H. Huang, Raymond de Verdier, Maria Correia Rudie, Jeffrey D. Bakas, Spyridon Calabrese, Evan Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiation Oncology SUNY Upstate Medical University SyracuseNY United States San FranciscoCA United States Department of Neurosurgery King’s College Hospital London United Kingdom School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Guy’s and St Thomas’ NHS Foundation Trust United Kingdom Department of Radiation Oncology University of Washington SeattleWA United States Department of Radiology Northwestern University EvanstonIL United States Department of Radiation Oncology Northwestern University EvanstonIL United States Department of Radiation Medicine and Applied Sciences University of California San Diego La Jolla CA United States Department of Radiology University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States Department of Neurological Surgery Indiana University School of Medicine IndianapolisIN United States Department of Radiation Oncology Indiana University IndianapolisIN United States Children’s National Hospital WashingtonDC United States George Washington University WashingtonDC United States Mayo Clinic RochesterMN United States CMH Lahore Medical College Lahore Pakistan Duke University Medical Center School of Medicine DurhamNC United States Sage Bionetworks United States University of Zürich Zürich Switzerland Artificial Intelligence Lab Department of Radiology Mayo Clinic RochesterMN United States MLCommons United States Factored AI United States Center For Federated Learning in Medicine Indiana University IndianapolisIN United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Medical Working Group MLCommons San FranciscoCA United States Intel United States Duke Universi

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of 750 radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label "target volume" representing the gross tumor volume (GTV) and any at-risk postoperative site. Target volume annotations adhere to established radiotherapy planning protocols, ensuring consistency across cases and institutions. For preoperative meningiomas, the target volume encompasses the entire GTV and associated nodular dural tail, while for postoperative cases, it includes at-risk resection cavity margins as determined by the treating institution. Case annotations were reviewed and approved by expert neuroradiologists and radiation oncologists. Participating teams will develop, containerize, and evaluate automated segmentation models using this comprehensive dataset. Model performance will be assessed using an adapted lesion-wise Dice Similarity Coefficient and the 95% Hausdorff distance. The top-performing teams will be recognized at the Medical Image Computing and computer Assisted Intervention Conference in October 2024. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes. © 2024, CC BY.

关键词： Radiotherapy

Understanding metric-related pitfalls in image analysis validation

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

Biomedical image analysis competitions: The state of current participation practice

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking