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Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions

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arXiv 2018年

作者： Maier-Hein, Lena Eisenmann, Matthias Reinke, Annika Onogur, Sinan Stankovic, Marko Scholz, Patrick Arbel, Tal Bogunovic, Hrvoje Bradley, Andrew P. Carass, Aaron Carolin Feldmann Frangi, Alejandro F. Ful, Peter M. van Ginneken, Bram Hanbury, Allan Honauer, Katrin Kozubek, Michal Landman, Bennett A. Marz, Keno Maier, Oskar Maier-Hein, Klaus Menze, Bjoern H. Müller, Henning Neher, Peter F. Niessen, Wiro Rajpoot, Nasir Sharp, Gregory C. Sirinukunwattana, Korsuk Speidel, Stefanie Stock, Christian Stoyanov, Danail Taha, Abdel Aziz van der Sommen, Fons Wang, Ching-Wei Weber, Marc-André Zheng, Guoyan Jannin, Pierre Kopp-Schneider, Annette Heidelberg Germany Centre for Intelligent Machines McGill University MontrealQC Canada Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna Vienna Austria Science and Engineering Faculty Queensland University of Technology BrisbaneQLD Australia Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore United States CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Sheffield Sheffield United Kingdom Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center Nijmegen Netherlands Institute of Information Systems Engineering TU Wien Vienna Austria Heidelberg University Heidelberg Germany Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Electrical Engineering Vanderbilt University NashvilleTN United States Institute of Medical Informatics Universität zu Lübeck Lübeck Germany Heidelberg Germany Institute for Advanced Studies Department of Informatics Technical University of Munich Munich Germany Information System Institute HES-SO Sierre Switzerland Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC Rotterdam Netherlands Department of Computer Science University of Warwick Coventry United Kingdom Department of Radiation Oncology Massachusetts General Hospital BostonMA United States Institute of Biomedical Engineering University of Oxford Oxford United Kingdom National Center for Tumor Diseases Dresden Dresden Germany Heidelberg Germany Department of Computer Science University College London London United Kingdom Data Science Studio Research Studios Austria FG Vienna Austria Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands AIExplore NTUST Center of Computer Vision and Medical Imaging Graduate Institute of Biomedical Engineering National Ta

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future. Copyright © 2018, The Authors. All rights reserved.

关键词： Quality control

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

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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

Proactive Eavesdropping via Cognitive Jamming in Fading Channels

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Proactive Eavesdropping via Cognitive Jamming in Fading Chan...

Ad-Hoc and Sensor Networks Symposium

作者： Jie Xu Lingjie Duan Rui Zhang Engineering Systems and Design Pillar Singapore University of Technology and Design Department of Electrical and Computer Engineering National University of Singapore

ISBN: (纸本)9781479966653

There is a growing need for government agencies to monitor suspicious communication links to prevent crimes and terror attacks. In this paper, we study a legitimate surveillance scenario where a legitimate monitor aims to intercept the suspicious communication between a transmitter and a receiver over fading channels. The legitimate monitor can eavesdrop (decode) the information of the suspicious link only when its achievable data rate is no smaller than that at the suspicious receiver. In practice, the legitimate eavesdropping is challenging, especially when the legitimate monitor is far from the suspicious transmitter. To overcome this issue, we propose a new approach, namely proactive eavesdropping via cognitive jamming, in which the legitimate monitor purposely jams the receiver and changes the suspicious communication (e.g., to a smaller data rate) in order to overhear easily. In particular, we consider delay-sensitive and delay-insensitive applications for the suspicious transmission, under which the legitimate monitor maximizes the eavesdropping non-outage probability and the relative eavesdropping rate, respectively, by optimizing its jamming power allocation over different fading states subject to an average power constraint. We present efficient algorithms for optimally solving the formulated problems. Numerical results show that thanks to the cognitive jamming, the proposed proactive eavesdropping scheme greatly outperforms the conventional passive eavesdropping without jamming.

关键词： Proactive eavesdropping Cognitive jamming Power control Full-duplex radio Legitimate surveillance

Cooperation in 5G Heterogeneous Networking: Relay Scheme Combination and Resource Allocation

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Cooperation in 5G Heterogeneous Networking: Relay Scheme Com...

作者： Chen, Zhengchuan Li, Tao Fan, Pingyi Quek, Tony Q. S. Letaief, Khaled Ben Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Hamad Bin Khalifa University Doha5825 Qatar

In 5G heterogeneous networking, it is promising to integrate different wireless networks to provide higher data rate. This paper models the integrated system as a receiver frequency division relay channel (RFDRC) and studies how to improve the transmission rate by combining decode-forward (DF), compress-forward (CF), and amplify-forward (AF) schemes. First, we establish clear criterions on how to select a relay scheme among DF, CF, and AF schemes and prove that the CF outperforms AF for all possible configurations. Based on the scheme selection criterions, we propose a hybrid DF-CF scheme which takes advantage of both DF and CF schemes in RFDRC. A near-optimal resource allocation is presented for the DF-CF-based system, leading to a new achievable rate for RFDRC. For ease of implementation, we further put forward a hybrid DF-AF scheme and reconsider the joint bandwidth and power allocation. Two suboptimal resource allocation solutions are established. In particular, when source frequency band and relay frequency band have equivalent bandwidth, we show that the proposed hybrid DF-AF scheme can achieve the concave envelope of the maximum between DF rate and AF rate. Numerical results show that the proposed schemes bring significant gains for RFDRC. © 2016 IEEE.

关键词： Resource allocation

Harnessing Self-Interference in Full-Duplex Relaying: An Analog Filter-and-Forward Approach

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Harnessing Self-Interference in Full-Duplex Relaying: An Ana...

IEEE Global Communications Conference

作者： Jie Xu Lingjie Duan Rui Zhang School of Information Engineering Guangdong University of Technology Engineering Systems and Design Pillar Singapore University of Technology and Design Department of Electrical and Computer Engineering National University of Singapore

ISBN: (纸本)9781509013296

This paper studies a full-duplex filter-and-forward (FD-FF) relay system in frequency-selective channels. Conventionally, the loop-back signal at the FD relay is treated as harmful self-interference and needs to be significantly suppressed via both analog- and digital-domain cancellation. However, the performance of the conventional self-interference cancellation approach is fundamentally limited due to the quantization error induced by the analog-to-digital converter (ADC) with limited dynamic range. In this paper, we consider an analog filter-and-forward design to help avoid the quantization error, and surprisingly show that the maximum achievable rate of such an FD-FF relay system is in fact regardless of the loop-back channel at the FD relay. We characterize the maximum achievable rate of this channel by jointly optimizing the transmit power allocation over frequency at the source and the frequency response of the filter at the relay, subject to their individual power constraints. Although this problem is non-convex, we obtain its optimal solution by applying the Lagrange duality method. By simulations it is shown that the proposed joint source and relay optimization achieves rate gains over other heuristic designs, and is also advantageous over the conventional approach by cancelling the relay loop-back signal as self-interference, especially when the residual self-interference after cancellation is still significant.

关键词： Relays OFDM Quantization (signal) Frequency response Delays Resource management Lagrangian functions Lagrangian frequency response relay Quantization self-interference management of resources Orthogonal frequency division multiplexing analogue filter CANCELLATIONS FULL DUPLEX delay

Deadline Scheduling as Restless Bandits

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Deadline Scheduling as Restless Bandits

Annual Allerton Conference on Communication, Control, and Computing

作者： Zhe Yu Yunjian Xu Lang Tongt the School of Electrical and Computer Engineering Cornell University Ithaca NY 14853 USA the School of Engineering Systems and Design Pillar Singapore University of Technology and Design Singapore 487372

ISBN: (纸本)9781509045518

The problem of stochastic deadline scheduling is considered. A constrained Markov decision process model is introduced in which jobs arrive randomly at a service center with stochastic job sizes, rewards, and completion deadlines. The service provider faces random processing costs, convex non-completion penalties, and a capacity constraint that limits the simultaneous processing of jobs. Formulated as a restless multi-armed bandit problem, the stochastic deadline scheduling problem is shown to be indexable. A closed-form expression of the Whittle's index is obtained for the case when the processing costs are constant. An upper bound on the gap-to-optimality of the Whittle's index policy is established, and the bound is shown to converge to zero as the job arrival rate and the the number of simultaneously available processors increase simultaneously.

关键词： problem stochastic deadline

Ten simple rules for reproducible research in jupyter notebooks

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arXiv 2018年

作者： Rule, Adam Birmingham, Amanda Zuniga, Cristal Altintas, Ilkay Huang, Shih-Cheng Knight, Rob Moshiri, Niema Nguyen, Mai H. Rosenthal, Sara Brin Pérez, Fernando Rose, Peter W. Design Lab UC San Diego San DiegoCA United States Center for Computational Biology and Bioinformatics UC San Diego San DiegoCA United States Department of Pediatrics UC San Diego San DiegoCA United States Data Science Hub San Diego Supercomputer Center UC San Diego San DiegoCA United States Departments of Bioengineering and Computer Science and Engineering Center for Microbiome Innovation UC San Diego San DiegoCA United States Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA United States Department of Statistics Berkeley Institute for Data Science UC Berkeley Lawrence Berkeley National Laboratory BerkeleyCA United States Biomedical Informatics Graduate Program Stanford University StanfordCA United States

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses. Copyright © 2018, The Authors. All rights reserved.

关键词： Digital storage

Sparse representation based multi-sensor image fusion: A review

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arXiv 2017年

作者： Zhang, Qiang Liu, Yi Blum, Rick S. Han, Jungong Tao, Dacheng Key Laboratory of Electronic Equipment Structure Design Ministry of Education Xidian University China Center for Complex Systems School of Mechano-Electronic Engineering Xidian University Xi'an Shaanxi710071 China Electrical and Computer Engineering Depart-ment Lehigh University BethlehemPA18015 United States Department of Computer and Information Sciences Northumbria University Newcastle upon TyneNE1 8ST United Kingdom School of Information Technologies in Faculty of Engineering and Information Technologies University of Sydney J12/318 Cleveland St DarlingtonNSW2008 Australia

As a result of several successful applications in computer vision and image processing, sparse representation (SR) has attracted significant attention in multi-sensor image fusion. Unlike the traditional multiscale transforms (MSTs) that presume the basis functions, SR learns an over-complete dictionary from a set of training images for image fusion, and it achieves more stable and meaningful representations of the source images. By doing so, the SR-based fusion methods generally outperform the traditional MST-based image fusion methods in both subjective and objective tests. In addition, they are less susceptible to mis-registration among the source images, thus facilitating the practical applications. This survey paper proposes a systematic review of the SR-based multi-sensor image fusion literature, highlighting the pros and cons of each category of approaches. Specifically, we start by performing a theoretical investigation of the entire system from three key algorithmic aspects, (1) sparse representation models;(2) dictionary learning methods;and (3) activity levels and fusion rules. Subsequently, we show how the existing works address these scientific problems and design the appropriate fusion rules for each application such as multi-focus image fusion and multi-modality (e.g., infrared and visible) image fusion. At last, we carry out some experiments to evaluate the impact of these three algorithmic components on the fusion performance when dealing with different applications. This article is expected to serve as a tutorial and source of reference for researchers preparing to enter the field or who desire to employ the sparse representation theory in other fields. Copyright © 2017, The Authors. All rights reserved.

关键词： Image fusion

AtLAST Science Overview Report

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arXiv 2024年

作者： Booth, Mark Klaassen, Pamela Cicone, Claudia Mroczkowski, Tony Cordiner, Martin A. Di Mascolo, Luca Johnstone, Doug van Kampen, Eelco Lee, Minju M. Liu, Daizhong Orlowski-Scherer, John Saintonge, Amélie Smith, Matthew W.L. Thelen, Alexander Wedemeyer, Sven Akiyama, Kazunori Andreon, Stefano Arzoumanian, Doris Bakx, Tom J.L.C. Bot, Caroline Bower, Geoffrey Brajša, Roman Chen, Chian-Chou da Cunha, Elisabete Eden, David Ettori, Stefano Gaches, Brandt Hatziminaoglou, Evanthia Luppe, Patricia Magnelli, Benjamin Marshall, Jonathan P. Montenegro-Montes, Francisco Miguel Niemack, Michael Nixon, Conor de Pater, Imke Perrott, Yvette Raimundo, Sandra I. Redaelli, Elena Richards, Anita Rybak, Matus Šarčević, Nikolina Semenov, Dmitry Spezzano, Silvia Srinivasan, Sundar Stanke, Thomas Andreani, Paola Beltrán, Maria T. Butler, Bryan J. Cantalupo, Sebastiano Dagostino, Miguel Chavez Duarte-Cabral, Ana Emonts, Bjorn Fletcher, Leigh Gary, Dale E. Gunar, Stanislav Hacar, Alvaro Hagedorn, Bendix Kaminski, Tomek Kirton, Fiona de Kleer, Katherine Kontar, Eduard Kuan, Yi-Jehng Lightfoot, John Lopez-Rodriguez, Enrique Lundgren, Andreas Milam, Stefanie N. Mohan, Atul Moreno, Raphael Motorina, Galina G. Moullet, Arielle Pattle, Kate Pellizzoni, Alberto Peretto, Nicolas Ramasawmy, Joanna Ricci, Claudio Rigby, Andrew J. Sánchez-Monge, Álvaro Saberi, Maryam Shimojo, Masumi Simionescu, Aurora Thompson, Mark Traficante, Alessio Vignali, Cristian White, Stephen M. UK Astronomy Technology Centre Royal Observatory Edinburgh Blackford Hill EdinburghEH9 3HJ United Kingdom Institute of Theoretical Astrophysics University of Oslo PO Box 1029 Blindern Oslo0315 Norway Karl-Schwarzschild-Strasse 2 Garching85748 Germany Astrochemistry Laboratory NASA Goddard Space Flight Center Code 691 GreenbeltMD20771 United States Laboratoire Lagrange Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Blvd de l’Observatoire CS 34229 Nice06304 cedex 4 France Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 Trieste34131 Italy INAF – Osservatorio Astronomico di Trieste via Tiepolo 11 Trieste34131 Italy IFPU – Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy NRC Herzberg Astronomy and Astrophysics 5071 West Saanich Rd VictoriaBCV9E 2E7 Canada Department of Physics and Astronomy University of Victoria VictoriaBCV8P 5C2 Canada Denmark DTU-Space Technical University of Denmark Elektrovej 327 Kgs. LyngbyDK2800 Denmark Max-Planck-Institut für extraterrestrische Physik Giessenbachstrasse 1 Garching BayernD-85748 Germany Purple Mountain Observatory Chinese Academy of Sciences 10 Yuanhua Road Nanjing210008 China Department of Physics and Astronomy University of Pennsylvania 209 South 33rd Street PhiladelphiaPA19104 United States Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Auf dem Hügel 69 BonnD-53121 Germany School of Physics & Astronomy Cardiff University The Parade CardiffCF24 3AA United Kingdom Division of Geological and Planetary Sciences California Institute of Technology PasadenaCA91125 United States Rosseland Centre for Solar Physics Institute of Theoretical Astrophysics University of Oslo Postboks 1029 Blindern OsloN-0315 Norway Haystack Observatory Massachusetts Institute of Technology 99 Millstone Road WestfordMA01886 United States National Astronomical Observatory of

Submillimeter and millimeter wavelengths provide a unique view of theUniverse, from the gas and dust that fills and surrounds galaxies to thechromosphere of our own Sun. Current single-dish facilities have presented atantalising view of the brightest (sub-)mm sources, and interferometers haveprovided the exquisite resolution necessary to analyse the details in smallfields, but there are still many open questions that cannot be answered withcurrent facilities. In this report we summarise the science that is guiding thedesign of the Atacama Large Aperture Submillimeter Telescope (AtLAST). Wedemonstrate how tranformational advances in topics including star formation inhigh redshift galaxies, the diffuse circumgalactic medium, Galactic ecology,cometary compositions and solar flares motivate the need for a 50m, single-dishtelescope with a 1-2 degree field of view and a new generation of highlymultiplexed continuum and spectral cameras. AtLAST will have the resolution todrastically lower the confusion limit compared to current single-dishfacilities, whilst also being able to rapidly map large areas of the sky anddetect extended, diffuse structures. Its high sensitivity and large field ofview will open up the field of submillimeter transient science by increasingthe probability of serendipitous detections. Finally, the science cases listedhere motivate the need for a highly flexible operations model capable of shortobservations of individual targets, large surveys, monitoring programmes,target of opportunity observations and coordinated observations with otherobservatories. AtLAST aims to be a sustainable, upgradeable, multipurposefacility that will deliver orders of magnitude increases in sensitivity andmapping speeds over current and planned submillimeter observatories. © 2024, CC BY.

关键词： Photons