检索结果-内蒙古大学图书馆

Feasibility of multiomics tumor profiling for guiding treatment of melanoma

Nature medicine 2025年 2025 May 27页

作者： Nicola Miglino Nora C Toussaint Alexander Ring Ximena Bonilla Marina Tusup Benedict Gosztonyi Tarun Mehra Gabriele Gut Francis Jacob Stephane Chevrier Kjong-Van Lehmann Ruben Casanova Andrea Jacobs Sujana Sivapatham Laura Boos Parisa Rahimzadeh Manuel Schuerch Bettina Sobottka Natalia Chicherova Shuqing Yu Rebekka Wegmann Julien Mena Emanuela S Milani Sandra Goetze Cinzia Esposito Jacobo Sarabia Del Castillo Anja L Frei Marta Nowak Anja Irmisch Jack Kuipers Monica-Andreea Baciu-Drăgan Pedro F Ferreira Franziska Singer Anne Bertolini Michael Prummer Ulrike Lischetti Rudolf Aebersold Marina Bacac Gerd Maass Holger Moch Michael Weller Alexandre P A Theocharides Markus G Manz Niko Beerenwinkel Christian Beisel Lucas Pelkmans Berend Snijder Bernd Wollscheid Viola Heinzelmann Bernd Bodenmiller Mitchell P Levesque Viktor H Koelzer Gunnar Rätsch Reinhard Dummer Andreas Wicki Department of Medical Oncology and Hematology University of Zurich and University Hospital Zurich Switzerland. NEXUS Personalized Health Technologies ETH Zurich Zurich Switzerland. SIB Swiss Institute of Bioinformatics Lausanne Switzerland. Swiss Data Science Center SDSC Zurich Switzerland. Department of Computer Science Institute of Machine Learning ETH Zurich Zurich Switzerland. Department of Dermatology University Hospital Zurich University of Zurich Zurich Switzerland. Department of Biomedicine University Hospital Basel and University of Basel Basel Switzerland. Department of Quantitative Biomedicine University of Zurich Zurich Switzerland. Department of Biology RWTH Aachen Aachen Germany. Department of Pathology and Molecular Pathology University of Zurich and University Hospital Zurich Switzerland. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. Department of Health Sciences and Technology ETH Zurich Zurich Switzerland. ETH PHRT Swiss Multi-Omics Center (SMOC) ETH Zurich Zurich Switzerland. Department of Molecular Life Sciences University of Zurich Zurich Switzerland. Roche Pharmaceutical Research and Early Development Roche Innovation Center Zurich Switzerland. Department of Biosystems Science and Engineering ETH Zurich Basel Switzerland. Roche Diagnostics GmbH MWG Penzberg Germany. Department of Neurology University Hospital and University of Zurich Zurich Switzerland. Institute of Medical Genetics and Pathology University Hospital Basel Basel Switzerland. Biomedical Informatics University Hospital Zurich Zurich Switzerland. AI Center at ETH Zurich ETH Zurich Zurich Switzerland. Department of Biology ETH Zurich Zurich Switzerland. Department of Medical Oncology and Hematology University of Zurich and University Hospital Zurich Switzerland. andreas.wicki@usz.ch.

There is limited evidence supporting the feasibility of using omics and functional technologies to inform treatment decisions. Here we present results from a cohort of 116 melanoma patients in the prospective, multicentric observational Tumor Profiler (TuPro) precision oncology project. Nine independent technologies, mostly at single-cell level, were used to analyze 126 patient samples, generating up to 500 Gb of data per sample (40,000 potential markers) within 4 weeks. Among established and experimental markers, the molecular tumor board selected 54 to inform its treatment recommendations. In 75% of cases, TuPro-based data were judged to be useful in informing recommendations. Patients received either standard of care (SOC) treatments or highly individualized, polybiomarker-driven treatments (beyond SOC). The objective response rate in difficult-to-treat palliative, beyond SOC patients (n = 37) was 38%, with a disease control rate of 54%. Progression-free survival of patients with TuPro-informed therapy decisions was 6.04 months, (95% confidence interval, 3.75-12.06) and 5.35 months (95% confidence interval, 2.89-12.06) in ≥third therapy lines. The proof-of-concept TuPro project demonstrated the feasibility and relevance of omics-based tumor profiling to support data-guided clinical decision-making. *** identifier: NCT06463509 .

关键词：

来源：评论

学校读者我要写书评

暂无评论

Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent.

arXiv

引用

arXiv 2019年

作者： Hu, Wenqing Zhu, Zhanxing Xiong, Haoyi Huan, Jun Department of Mathematics and Statistics Missouri University of Science and Technology University of Missouri Rolla Peking University Beijing Institute of Big Data Research Beijing China Big Data Lab Baidu Inc. National Engineering Laboratory of Deep Learning Application and Technology

We interpret the variational inference of the Stochastic Gradient Descent (SGD) as minimizing a new potential function named the quasi-potential. We analytically construct the quasi-potential function in the case when the loss function is convex and admits only one global minimum point. We show in this case that the quasi-potential function is related to the noise covariance structure of SGD via a partial differential equation of Hamilton-Jacobi type. This relation helps us to show that anisotropic noise leads to faster escape than isotropic noise. We then consider the dynamics of SGD in the case when the loss function is non-convex and admits several different local minima. In this case, we demonstrate an example that shows how the noise covariance structure plays a role in "implicit regularization", a phenomenon in which SGD favors some particular local minimum points. This is done through the relation between the noise covariance structure and the quasi-potential function. Our analysis is based on Large Deviations Theory (LDT), and they are validated by numerical experiments. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Sequential monte carlo method for bayesian multiple testing of pairwise interactions among large number of neurons 14

Sequential monte carlo method for bayesian multiple testing ...

引用

14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Liu, Bin Vinci, Giuseppe Snyder, Adam C. Kass, Robert E. School of Computer Science Jiangsu Key Lab of Big Data Security Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China Department of Statistics Rice University Houston United States Visual Neuroscience Lab University of Pittsburgh Pittsburgh United States Basis of Cognition Carnegie Mellon University Machine Learning Department Center for the Neural Department of Statistics Pittsburgh United States

ISBN: (纸本)9781538680971

The problem of multiple testing arises in many contexts, including testing for pairwise interaction among a large number of neurons. Recently a method was developed to control false positives when covariate information, such as distances between pairs of neurons, is available. This method, however, relies on computationally-intensive Markov Chain Monte Carlo (MCMC). Here we develop an alternative, based on Sequential Monte Carlo, which only requires one pass of the data. This scheme considers data items sequentially, with relevant probabilities being updated at each step. Simulation experiments demonstrate that the proposed algorithm delivers results as accurately as the previous MCMC method. We illustrate the method by using it to analyze neural recordings from extrastriate cortex in a macaque monkey. © 2018 IEEE.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

Building Surrogate Models of Nuclear Density Functional Theory with Gaussian Processesand Autoencoders

arXiv

引用

arXiv 2022年

作者： Verriere, Marc Schunck, Nicolas Kim, Irene Marević, Petar Quinlan, Kevin NGo, Michelle N. Regnier, David Lasseri, Raphael David Nuclear and Data Theory group Nuclear and Chemical Science Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Machine Intelligence group Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Sensing and Intelligent Systems group Global Security Computing Applications Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Centre Borelli ENS Paris-Saclay Université Paris-Saclay Gif-sur-Yvette91190 France Physics Department Faculty of Science University of Zagreb Zagreb10000 Croatia Applied Statistics Group Lawrence Livermore National Laboratory LivermoreCA94551 United States Center for Complex Biological Systems University of California Irvine IrvineCA92697 United States Université Paris-Saclay CEA Laboratoire Matière en Conditions Extrêmes Bruyères-le-Châtel91680 France CEA DAM DIF Arpajon91297 France

From the lightest Hydrogen isotopes up to the recently synthesized Oganesson (Z = 118), it is estimated that as many as about 3000 atomic nuclei could exist in nature. Most of these nuclei are too short-lived to be occurring on Earth, but they play an essential role in astrophysical events such as supernova explosions or neutron star mergers that are presumed to be at the origin of most heavy elements in the Universe. Understanding the structure, reactions, and decays of nuclei across the entire chart of nuclides is an enormous challenge because of the experimental difficulties in measuring properties of interest in such fleeting objects and the theoretical and computational issues of simulating strongly-interacting quantum many-body systems. Nuclear density functional theory (DFT) is a fully microscopic theoretical framework which has the potential of providing such a quantitatively accurate description of nuclear properties for every nucleus in the chart of nuclides. Thanks to high-performance computing facilities, it has already been successfully applied to predict nuclear masses, global patterns of radioactive decay like β or γ decay, and several aspects of the nuclear fission process such as, e.g., spontaneous fission half-lives. Yet, predictive simulations of nuclear spectroscopy – the low-lying excited states and transitions between them – or of nuclear fission, or the quantification of theoretical uncertainties and their propagation to basic or applied nuclear science applications, would require several orders of magnitude more calculations than currently possible. However, most of this computational effort would be spent into generating a suitable basis of DFT wavefunctions. Such a task could potentially be considerably accelerated by borrowing tools from the field of machine learning and artificial intelligence. In this paper, we review different approaches to applying supervised and unsupervised learning techniques to nuclear DFT. © 2022, CC BY.

关键词： Density functional theory

来源：评论

学校读者我要写书评

暂无评论

Correction to: New theoretical ISM‑K2 Bayesian network model for evaluating vaccination effectiveness

引用

Journal of Ambient Intelligence and Humanized Computing 2022年第10期14卷 14383-14383页

作者： Xie, Xiaoliang Xie, Bingqi Xiong, Dan Hou, Muzhou Zuo, Jinxia Wei, Guo Chevallier, Julien School of Mathematics and Statistics Hunan University of Technology and Busin Ess Changsha China Key Laboratory of Hunan Province for Statistical Learning and Intelligent Computation Hunan University of Technology and Business Changsha China Institute of Big Data and Internet Innovation Hunan University of Technology and Business Changsha China School of Mathematics and Statistics Central South University Changsha China Department of Mathematics and Computer Science University of North Carolina at Pembroke Pembroke USA IPAG Business School (IPAG Lab) Paris France University Paris 8 (LED) Saint-Denis France

来源：评论

学校读者我要写书评

暂无评论

Nonparametric density estimation with adversarial losses 18

Nonparametric density estimation with adversarial losses

引用

Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Shashank Singh Ananya Uppal Boyue Li Chun-Liang Li Manzil Zaheer Barnabás Póczos Machine Learning Department Carnegie Mellon University and Department of Statistics & Data Science Carnegie Mellon University Department of Mathematical Sciences Carnegie Mellon University Language Technologies Institute Carnegie Mellon University Machine Learning Department Carnegie Mellon University

We study minimax convergence rates of nonparametric density estimation under a large class of loss functions called "adversarial losses", which, besides classical Lp losses, includes maximum mean discrepancy (MMD), Wasserstein distance, and total variation distance. These losses are closely related to the losses encoded by discriminator networks in generative adversarial networks (GANs). In a general framework, we study how the choice of loss and the assumed smoothness of the underlying density together determine the minimax rate. We also discuss implications for training GANs based on deep ReLU networks, and more general connections to learning implicit generative models in a minimax statistical sense.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DeePMD-kit v2: A software package for Deep Potential models

arXiv

引用

arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

来源：评论

学校读者我要写书评

暂无评论

Time series featurization via topological data analysis

arXiv

引用

arXiv 2018年

作者： Kim, Kwangho Kim, Jisu Rinaldo, Alessandro Department of Statistics & Data Science Machine Learning Department} Carnegie Mellon University PittsburghPA15213 United States Inria Saclay – Île-de-France Palaiseau France Department of Statistics & Data Science Carnegie Mellon University Pittsburgh United States

We develop a novel algorithm for feature extraction in time series data by leveraging tools from topological data analysis. Our algorithm provides a simple, efficient way to successfully harness topological features of the attractor of the underlying dynamical system for an observed time series. The proposed methodology relies on the persistent landscapes and silhouette of the Rips complex obtained after a de-noising step based on principal components applied to a time-delayed embedding of a noisy, discrete time series sample. We analyze the stability properties of the proposed approach and show that the resulting TDA-based features are robust to sampling noise. Experiments on synthetic and real-world data demonstrate the effectiveness of our approach. We expect our method to provide new insights on feature extraction from granular, noisy time series data. Copyright © 2018, The Authors. All rights reserved.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Hardware architecture proposal for TEDA algorithm to data streaming anomaly detection

arXiv

引用

arXiv 2020年

作者： da Silva, Lucileide M.D. Coutinho, Maria G.F. Santos, Carlos E.B. Santos, Mailson R. Guedes, Luiz Affonso Dolores Ruiz, M. Fernandes, Marcelo A.C. Laboratory of Machine Learning and Intelligent Instrumentation Federal University of Rio Grande do Norte Natal59078-970 Brazil Federal Institute of Education Science and Technology of Rio Grande do Norte Paraiso Santa Cruz RN59200-000 Brazil Department of Statistics and Operations Research University of Granada Spain Department of Computer Engineering and Automation Federal University of Rio Grande do Norte Natal RN59078-970 Brazil John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States

The amount of data in real-time, such as time series and streaming data, available today continues to grow. Being able to analyze this data the moment it arrives can bring an immense added value. However, it also requires a lot of computational effort and new acceleration techniques. As a possible solution to this problem, this paper proposes a hardware architecture for Typicality and Eccentricity data Analytic (TEDA) algorithm implemented on Field Programmable Gate Arrays (FPGA) for use in data streaming anomaly detection. TEDA is based on a new approach to outlier detection in the data stream context. In order to validate the proposals, results of the occupation and throughput of the proposed hardware are presented. Besides, the bit accurate simulation results are also presented. The project aims to Xilinx Virtex-6 xc6vlx240t-1ff1156 as the target FPGA. Copyright © 2020, The Authors. All rights reserved.

关键词： Reconfigurable architectures

来源：评论

学校读者我要写书评

暂无评论

The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

arXiv

引用

arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：