Overview and Objective

Along with the quick evolvement of artificial intelligence (AI), deep/machine learning, and big data in healthcare, medical AI research goes beyond methodological/algorithm development. FDA has authorized more than 600 medical AI software, and many new research questions are emerging in the practical and applied aspects of medical AI, such as translational study, clinical evaluation, real-world use cases of AI systems, ethical, legal, and social issues (ELSI), etc. Clinicians are playing an increasingly stronger role in the frontiers of applied AI through collaboration with AI experts, data scientists, informatics officers, and the industry workforce.

Practical applications of medical AI bring in new challenges and opportunities. Today is the best time to strengthen the connections between the arising aspects of AI translation and applications and classic methodological/algorithmic research. The AMAI workshop aims to engage medical AI practitioners and bring more application flavor in clinical, evaluation, human-AI collaboration, new technical strategy, trustfulness, etc., to augment the research and development on the application aspects of medical AI, on top of pure technical research. 

The goal of AMAI is to create a forum to bring together researchers, clinicians, data scientists, domain experts, AI practitioners, industry, and students to investigate and discuss various aspects related to applications of medical AI. AMAI will 1) introduce emerging medical AI research topics and novel methodology towards applications, 2) showcase the evaluation, translation, use case, success, ELSI considerations of AI in healthcare, 3) develop multi-disciplinary collaborations and academic-industry partnerships, and 4) provide educational, networking, and career opportunities for attendees including clinicians, scientists, trainees, and students. 

AMAI 2024 will be composed of invited talks, contribution paper/abstract presentations, and expert panel discussions. Submissions will include 2 tracks, full papers and abstracts. Among all the accepted full papers and abstracts, the workshop will give a Best Student Paper award, a Best Workshop Paper award, and a Best Abstract Award, all with certificates. 

The first AMAI workshop was held on September 18, 2022 in Singapore, as a Satellite Event of MICCAI 2022 and it was a big success. 

The second AMAI workshop was held on October 8, 2023 in Vancouver, Canada, as a Satellite Event of MICCAI 2023 and the success was continued.

Call for Submissions

AMAI calls for submissions from multiple aspects of research topics, such as, but not limited to, those listed below. AMAI is agnostic to medical data modalities and encourages submissions using imaging and/or non-imaging data.

• Clinical and translational AI/ML applications focusing on addressing specific diseases or medical contexts

• Medical AI/ML evaluation studies in various settings (including simulation and real-world clinical environments)

• Multi-modal medical AI systems, tools, and techniques

• Generative AI and synthetic data use for biomedical applications

• Foundation models, Vision-Language models, Large Language models, applications of ChatGPT or other similar systems  in medical domains

• Novel AI/ML strategies, approaches, methodologies, tools, and software towards practical utilities

• Human reader/observer study, human-AI interaction/collaboration, synergy of AI/ML and human/medical intelligence

• Success stories, use cases, challenges, opportunities, lessons learned, or prospects in medical AI/ML applications at work

• Strategies, techniques, evaluation, measurement, or opinions on useability, explainability, trustworthiness, safety/security, regulations, acceptance, limitations, bias, fairness, disparities, and ethical, legal, and social issues (ELSI) of medical AI/ML

• Medical AI/ML use from perspectives of researchers, clinicians, health providers, patients, and the public society

• Multi-disciplinary AI/ML collaboration work among data scientists, clinicians, and other domain experts

• Post-market evaluation, monitoring, and updating of medical AI in practice 

Submissions may be in two tracks:

Track 1: Full papers: Submissions must be new work. All submissions will be reviewed by at least two experts with experience of relevance. Accepted papers will be assigned as oral or poster presentations primarily based on merit. Each paper will allow a maximum of 8 pages (including texts, figures, and tables) for scientific content and up to 2 additional pages for references. Submissions should be formatted in Lecture Notes in Computer Science (LNCS) style (Please use Springer LaTeX or Word templates) and anonymized for double-blind review. Supplemental materials are not allowed. For accepted papers, the corresponding/senior authors will need to complete and sign a Consent-to-Publish form on behalf of all the authors. For papers invited for publication in the partnering journals, the authors will be asked to convert the accepted papers to align with the format requirements of the journals.

Note: Within the last section (i.e., Discussion or Conclusion) of your paper, it is required to include a separate paragraph at the end of the section to briefly describe the prospect of application of your work. Please refer to below format (note that the phrase of "Prospect of application" needs to be bold font):

Prospect of application: Use maximum 60 words to describe the prospect and envisioned contexts, scenarios, or circumstances on the potential application/deployment of your work. 

Track 2: Abstracts: Submissions may be new work or recently published/accepted papers (including posted preprints). All submissions will be reviewed in terms of scientific merit, relevance to the workshop, and significance to the field. Accepted abstracts may be assigned primarily as poster presentations. Submissions will allow a maximum of 1 page (including figures/tables, if any), following specified formats in this template: AMAI Abstract Template. Abstract submissions do not need to be anonymized. The accepted abstracts will be made publicly accessible on this website.

Submissions for both tracks should be submitted via the CMT system: https://cmt3.research.microsoft.com/amai2024

Camera-ready Submission Instructions

Full papers: Please follow the MICCAI 2024 main conference's general guidelines (if applicable) for camera-ready submissions: https://conferences.miccai.org/2024/en/CAMERA-READY-SUBMISSION-GUIDELINES.html. Paper length: a maximum of 8.5 pages (including texts, figures, and tables) for scientific content and up to 2 additional pages for references (this is consistent with the MICCAI main conference). Supplemental materials are not allowed.

The License to Publish form needs to be signed by the corresponding/senior author on behalf of all the authors. The corresponding author signing the copyright form should match the corresponding author marked on the paper. Conference Name (i.e., AMAI 2024) and the Volume Editors' Names are already entered for you at the first page, where you need to fill in the title and names of the all authors and corresponding authors of your paper. This form must be signed in wet-ink. Digital signature is not acceptable. Please scan your signed form and save it as a PDF file (file name format: AMAI2024_License-to-Publish_PaperID.pdf) and upload it to the CMT system.

The corresponding author must be available to carry out a final proof check of the typeset paper before publishing in the LNCS proceedings. He or she will be given a 72-hour time-slot to do so. The corresponding author should be clearly marked as such in the header of the paper. He or she is also the one who signs the license-to-publish form on behalf of all of the authors. Please note that the corresponding author cannot be changed after the camera ready submission deadline. We encourage the inclusion of all of the authors’ email addresses in the header, but at the very least, the email address of the corresponding author should be present.

Abstracts: Final version of the accepted abstracts should be formatted strictly following the AMAI Abstract Template. The maximum length of an abstract is 1 page (including everything). Both a Word source document (.docx) and a corresponding PDF document (.pdf) are required as final files to submit to the CMT system. File names should be formatted as: AMAI2024_Abstract_submissionID.docx and AMAI2023_Abstract_submissionID.pdf. Supplemental materials are not allowed. No copyright form will need to be signed for accepted abstracts. The final version of the accepted abstracts will be made publicly accessible on this website.

Please use the same CMT system link (see above) to submit the camera-ready papers or final abstracts.

Presentation Instructions

1) The workshop will start 1:30pm local time on Sunday Oct. 6 2024 at Cristal Room, Palmeraie Palace.

2) Presentations will be in person. No virtual or hybrid presentations.

3) The presentation mode (oral or poster) is shown in the portal of the CMT system.

4) Oral presentations will be a total of 10 minutes per paper (Q&A time included). There are no specific formatting requirements on the presentation slides. Presenters are asked to show up a bit earlier in the workshop room to copy the presentation files to the computer before the start of the workshop.

5) For poster presentations, please make your physical posters referring to the MICCAI main conference poster formats. Please make sure you use the MICCAI 2024 Poster Template: download here. Posters will be staying up throughout the full day (not just the workshop hours) on Oct. 6 and please display your posters before the workshop starts. There will be a dedicated poster session in the workshop agenda for authors to present their posters, but authors can and are encouraged to present/interact anytime while the posters are in display. There will be labels on the poster boards. Authors please just find an empty board labeled for our workshop (i.e., AMAI) to use for your posters (no specific numbers will be assigned to specific posters). 

Publishing Plan and Awards

By default, accepted full papers will be published by Springer Nature as a part of the MICCAI Satellite Events joint LNCS proceedings. Depending on the quality and topics, a number of accepted full papers may be recommended to a partnering journal (the papers will need to go through additional editorial and peer-review process run by the journal, and authors will be asked whether they would like their accepted papers to be considered for recommendation; more details will be announced here in due course). Papers to be published in the partnering journal will not be published in the Springer Nature LNCS proceedings.

Accepted abstracts will not be formally published by publishers (neither in the MICCAI Satellite Events joint LNCS proceedings nor in the partnering journals); they will be made publicly accessible on this website.

Among all the accepted full papers and abstracts, AMAI will give a Best Student Paper award, a Best Workshop Paper award, and a Best Abstract Award, all with electronic certificates. 

Important Dates

Workshop time: October 6, 2024; 1:30pm - 6:00pm

Submissions open: April 15, 2024

Submissions close: 11:59pm, Pacific Time, June 24, 2024

Submissions close: 11:59pm, Pacific Time, June 29, 2024

Notification of acceptance: July 12, 2024

Notification of acceptance: July 15, 2024

Camera ready submission due: 11:59pm, Pacific Time, July 26, 2024

Camera ready submission due: 11:59pm, Pacific Time, July 31, 2024

Program Committee

(In alphabetical order)

• Mohd Anwar, PhD, National Institute of Biomedical Imaging and Bioengineering (NIBIB), USA

• Dooman Arefan, PhD, University of Pittsburgh, USA

• Sixian Chan, PhD, Zhejiang University of Technology, China 

• Niketa Chotai, MD, RadLink Imaging Centre and National University of Singapore, Singapore 

• Dania Daye, MD, PhD, Massachusetts General Hospital/Harvard Medical School, USA 

• Douglas Hartman, MD, University of Cincinnati Medical Center, USA 

• Michail Klontzas, MD, University of Crete, Greece 

• Zhicheng Jiao, PhD, Brown University, USA

• Fabian Laqu, MD, University Hospital Würzburg, Germany 

• Ines Prata Machado, PhD, University of Cambridge, UK

• Mireia Crispin Ortuzar, PhD, University of Cambridge and Cancer Research, UK 

• Chang Min Park, MD, PhD, Seoul National University Hospital, South Korea 

• Matthew Pease, MD, Indiana University, USA 

• Nicholas Petrick, PhD, U.S. Food and Drug Administration, USA 

• Bhanu Prakash K.N. PhD, Agency for Science, Technology and Research (A*STAR), Singapore 

• Parisa Rashidi, PhD, University of Florida, USA 

• Zaid Siddiqui, MD, Baylor College of Medicine, USA 

• Tao Tan, PhD, Macao Polytechnic University, Macau  

• Zhiyong (Sean) Xie, PhD, Xellar Biosystems, USA  

• Qi Yang, PhD, Genentech, Inc., USA 

• Yudong Zhang, MD, PhD, First Affiliated Hospital, Nanjing Medical University, China  

• Jian Zheng, PhD, Suzhou Institute of Biomedical Engineering and Technology of the Chinese Academy of Sciences, China 

 Agenda

Location: Cristal Room, Palmeraie Palace

Date/time: Oct. 6 2024, Sunday, 1:30pm - 6pm

** Paper presenters please report to the organizers before the start of the workshop and copy your presentation slides to the onsite computer.

13:30-13:35: Introductory remarks: AMAI Organizers

13:35-14:00: Keynote talk: 

Parisa Rashidi, Ph.D., FAIMBE, UF Foundation Professor, University of Florida 

Title: "Applications of Medical AI and Computer Vision in Acute Care"

14:00-14:50: Full Paper Oral Presentation Session I: 5 papers

  (10 mins /paper; including presentation and Q&A) 

• Head CT Scan Motion Artifact Correction via Diffusion-Based Generative Models

Zhennong Chen, Siyeop Yoon, Quirin Strotzer, Rehab Naeem Khalid, Matthew Tivnan, Quanzheng Li, Rajiv Gupta, Dufan Wu

• SP-NAS: Surgical Phase Recognition-based Navigation Adjustment System for distal gastrectomy

Hyeongyu Chi, Bogyu Park, Keunyoung Kim, Jiwon Lee, Sungjea Kim, Hyeonu Jeong, Jihun Yoon, Chihyun Song, Seokrae Park, Youngno Yoon, Youngsoo Kim, Sung Hyun Park, Yoo Min Kim, Min-Kook Choi, Woojin Hyung, and Hansol Choi

• Automated Feedback System for Surgical Skill Improvement in Endoscopic Sinus Surgery

Tomoko Yamaguchi, Ryoichi Nakamura, Akihito Kuboki, and Nobuyoshi Otori

• Predicting Falls through Muscle Weakness from a Single Whole Body Image: A Multimodal Contrastive Learning Framework

Xia Zhang, Afsah Saleem, Zaid Ilyas, David Suter, Uzair Nadeem, Richard L. Prince, Kun Zhu, Joshua R. Lewis, Marc Sim, and Syed Zulqarnain Gilani

• Enhanced Interpretability in Histopathological Images via Combined Tissue and Cell-Level Graph Analysis

Mieko Ochi, Daisuke Komura, Tetsuo Ushiku, Yasushi Rino, and Shumpei Ishikawa 

14:50-15:55: Poster presentation (12 full papers and 9 abstracts) and coffee break

MICCAI coffee break time: 15:30-16:00

15:55-16:15: Invited talk 1

Behrouz Shabestari, PhD, Director, National Technology Centers Program, National Institute of Biomedical Imaging and Bioengineering (NIBIB)

Title: “NIBIB Support for AI in Imaging and Interventional Radiology”

16:15-16:55: Full Paper Oral Presentation Session II: 4 papers

 (10 mins /paper; including presentation and Q&A)

• Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging

Dovile Juodelyte, Yucheng Lu, Amelia Jiménez-Sánchez, Sabrina Bottazzi, Enzo Ferrante, and Veronika Cheplygina

• Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears

Louise Guillon, Soheib Biga, Axel Puyo, Grégoire Pasquier, Valentin Foucher, Yendoubé E. Kantchire, Stéphane E. Sossou, Ameyo M. Dorkenoo, Laurent Bonnardot, Marc Thellier, Laurence Lachaud, and Renaud Piarroux

• RadImageGAN – A Multi-modal Dataset-Scale Generative AI for Medical Imaging

Zelong Liu, Peyton Smith, Alexander Lautin, Jieshen Zhou, Maxwell Yoo, Mikey Sullivan, Haorun Li, Louisa Deyer, Alexander Zhou, Arnold Yang, Alara Yimaz, Catherine Zhang, James Grant, Daiqing Li, Zahi A. Fayad, Sean Huver, Timothy Deyer, Xueyan Mei

• Evaluating the Impact of Pulse Oximetry Bias in Machine Learning under Counterfactual Thinking

Inês Martins, João Matos, Tiago Gonçalves, Leo A. Celi, An-Kwok Ian Wong, and Jaime S. Cardoso

16:55-17:15: Invited talk 2

Mathias Unberath, PhD, John C. Malone Associate Professor

Johns Hopkins University

Title: “Human-Centered Research in Medical Imaging AI: Shared Guidelines and Standards”

17:15-17:45: Full Paper Oral Presentation Session III: 3 papers

 (10 mins /paper; including presentation and Q&A)

• Evaluating Perceived Workload, Usability and Usefulness of Artificial Intelligence Systems in Low-Resource Settings: Semi-Automated Classification and Detection of Community Acquired Pneumonia

Malaizyo G Muzumala, Ernest O Zulu, Peter Chibuta, Mayumbo Nyirenda, and Lighton Phiri

• EHRmonize: A Framework for Medical Concept Abstraction from Electronic Health Records using Large Language Models

João Matos, Jack Gallifant, Jian Pei, and A. Ian Wong

• Data-Efficient Radiology Report Generation via Similar Report Features Enhancement

Yanfeng Li, Jinghan Sun, and Liansheng Wang 

17:45-17:55: Award announcement, attendee discussions, and closing remarks.

17:55: Adjourn

Accepted Papers and Abstracts

Full papers: 12 oral presentation and 12 poster presentation, to appear in Springer-published proceedings (in press)

Exploring CNN and Transformer-based Architectures to Improve Image Segmentation for Chronic Wound Measurement                                                 

Rafaela Carvalho, Ana C. Morgado, Ana Filipa Sampaio, and Maria J. M. Vasconcelos

From Pixel Scores to Clinical Impacts: The Implicit Choices in FROC Metric Design and Their Consequences   

Minjeong Kim, Hesham Dar, Sanguk Park, and Thijs Kooi

Head CT Scan Motion Artifact Correction via Diffusion-Based Generative Models 

Zhennong Chen, Siyeop Yoon, Quirin Strotzer, Rehab Naeem Khalid, Matthew Tivnan, Quanzheng Li, Rajiv Gupta, Dufan Wu

SP-NAS: Surgical Phase Recognition-based Navigation Adjustment System for distal gastrectomy                                                                                               

Hyeongyu Chi, Bogyu Park, Keunyoung Kim, Jiwon Lee, Sungjea Kim, Hyeonu Jeong, Jihun Yoon, Chihyun Song, Seokrae Park, Youngno Yoon, Youngsoo Kim, Sung Hyun Park, Yoo Min Kim, Min-Kook Choi, Woojin Hyung, and Hansol Choi

Transforming Multimodal Models into Action Models for Radiotherapy     

Matteo Ferrante, Alessandra Carosi, Rolando Maria D’Angelillo, and Nicola Toschi

Enhanced Interpretability in Histopathological Images via Combined Tissue and Cell-Level Graph Analysis 

Mieko Ochi, Daisuke Komura, Tetsuo Ushiku, Yasushi Rino, and Shumpei Ishikawa

Targeted Visual Prompting for Medical Visual Question Answering

Sergio Tascon-Morales, Pablo Márquez-Neila, Raphael Sznitman

Deep Learning for Resolving 3D Microstructural Changes in the Fibrotic Liver

William M. Laprade, Behnaz Pirzamanebin, Rajmund Mokso, Julia Nilsson, Vedrana A. Dahl, Anders B. Dahl, Dan Holmberg, and Anja Schmidt-Christensen

Predicting Falls through Muscle Weakness from a Single Whole Body Image: A Multimodal Contrastive Learning Framework 

Xia Zhang, Afsah Saleem, Zaid Ilyas, David Suter, Uzair Nadeem, Richard L. Prince, Kun Zhu, Joshua R. Lewis, Marc Sim, and Syed Zulqarnain Gilani

Optimizing ICU Readmission Prediction: A Comparative Evaluation of AI Tools 

Hoda Helmy, Chaima Ben Rabah, Nada Ali, Ahmed Ibrahim, Abdullah Hoseiny, and Ahmed Serag

Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging

Dovile Juodelyte, Yucheng Lu, Amelia Jiménez-Sánchez, Sabrina Bottazzi, Enzo Ferrante, and Veronika Cheplygina

Evaluating Perceived Workload, Usability and Usefulness of Artificial Intelligence Systems in Low-Resource Settings: Semi-Automated Classification and Detection of Community Acquired Pneumonia

Malaizyo G Muzumala, Ernest O Zulu, Peter Chibuta, Mayumbo Nyirenda, and Lighton Phiri

Incremental Augmentation Strategies for Personalised Continual Learning in Digital Pathology Contexts

Arijit Patra

Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears

Louise Guillon, Soheib Biga, Axel Puyo, Grégoire Pasquier, Valentin Foucher, Yendoubé E. Kantchire, Stéphane E. Sossou, Ameyo M. Dorkenoo, Laurent Bonnardot, Marc Thellier, Laurence Lachaud, and Renaud Piarroux

Automated Feedback System for Surgical Skill Improvement in Endoscopic Sinus Surgery

Tomoko Yamaguchi, Ryoichi Nakamura, Akihito Kuboki, and Nobuyoshi Otori

Quantifying Knee Cartilage Shape and Lesion: From Image to Metrics

Yongcheng Yao and Weitian Chen

RadImageGAN – A Multi-modal Dataset-Scale Generative AI for Medical Imaging

Zelong Liu, Peyton Smith, Alexander Lautin, Jieshen Zhou, Maxwell Yoo, Mikey Sullivan, Haorun Li, Louisa Deyer, Alexander Zhou, Arnold Yang, Alara Yimaz, Catherine Zhang, James Grant, Daiqing Li, Zahi A. Fayad, Sean Huver, Timothy Deyer, Xueyan Mei

Ensemble-KAN: Leveraging Kolmogorov Arnold Networks to Discriminate Individuals with Psychiatric Disorders from Controls

Gianluca De Franceschi, Inês W. Sampaio, Stefan Borgwardt, Joseph Kambeitz, Lana Kambeitz-Ilankovic, Eva Meisenzahl, Raimo K. R. Salokangas, Rachel Upthegrove, Stephen J. Wood, Nikolaos Koutsouleris, Paolo Brambilla, and Eleonora Maggioni

SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury 

Enamundram Naga Karthik, Jan Valošek, Lynn Farner, Dario Pfyffer, Simon Schading-Sassenhausen, Anna Lebret4, Gergely David, Andrew C. Smith, Kenneth A. Weber II, Maryam Seif, Patrick Freund, and Julien Cohen-Adad

EHRmonize: A Framework for Medical Concept Abstraction from Electronic Health Records using Large Language Models

João Matos, Jack Gallifant, Jian Pei, and A. Ian Wong

Evaluating the Impact of Pulse Oximetry Bias in Machine Learning under Counterfactual Thinking

Inês Martins, João Matos, Tiago Gonçalves, Leo A. Celi, An-Kwok Ian Wong, and Jaime S. Cardoso

Normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification

Songlin Zhao, Rong Zhou, Yu Zhang, Yong Chen, and Lifang He

One-Shot Medical Video Object Segmentation via Temporal Contrastive Memory Networks

Yaxiong Chen, Junjian Hu, Chunlei Li, Zixuan Zheng, Jingliang Hu, Yilei Shi, Shengwu Xiong, Xiao Xiang Zhu, and Lichao Mou

Data-Efficient Radiology Report Generation via Similar Report Features Enhancement 

Yanfeng Li, Jinghan Sun, and Liansheng Wang

Abstracts : 9 poster presentations  (link to abstracts)

Automatic Vertebra Screw Planning in CT Throughout the Whole Spine

Alexandra Ertl, Paul Naser, Lisa Kausch, Peter Neher, Shuhan Xiao, Robin Peretzke, David Zimmerer, Markus Bujotzek, Moritz Scherer, Klaus Maier-Hein

Pre-Clinical Evaluation of Content-based Image Retrieval System for Pathological Diagnosis

Daisuke Komura, Ranny R. Herdiantoputri, Mieko Ochi, Yuki Fukawa, Kou Kayamori, Maiko Tsuchiya, Yoshinao Kikuchi, Tetsuo Ushiku, Tohru Ikeda, Shumpei Ishikawa

MM-ACE: Automated vertebrae identification and segmentation in longitudinal CT scans of patients with multiple myeloma

Djennifer K. Madzia-Madzou, Margot Jak, Bart de Keizer, Jorrit-Jan Verlaan, Monique C. Minnema, Kenneth Gilhuijs

FSS-In: Robust Few-Shot Segmentation with Incremental Noise Learning in Veterinary Radiology

Jun-Young Oh, In-Gyu Lee, Tae-Eui Kam, Namsoon Lee, Ji-Hoon Jeong

Image-based Outcome Prediction of Inhibitory Control Testing in Awake Surgery of Glioma Patients

T.M. (Tessa) Kos, M. (Mathijs) de Boer, L.W. (Wilbert) Bartels, P.A. (Pierre) Robe, T.P.C. van Doormaal

Real-world Federated Learning in Radiology: Hurdles to overcome and Benefits to gain

Markus R. Bujotzek,*, Ünal Akünal, Stefan Denner, Peter Neher, Maximilian Zenk, Klaus H. Maier-Hein, Andreas Bucher

Perfusion-Enhanced Multitask CNN Models for Prognosis in High-Grade Gliomas

Maria Gómez Mahiques, F. Javier Gil-Terrón, Carles Lopez-Mateu, Víctor Montosa-i-Mico, Juan Miguel García-Gómez, Elies Fuster-Garcia

Augmented Oocyte Retrieval for Improved Outcome of Assisted Reproductive Technologies

Anusuiya, Chinmay Gupta, Pooja P Jain, Puspamita Banerjee, Rahul Dutta, Subhamoy Mandal

One-Prompt to Segment All Medical Images

Junde Wu, Jiayuan Zhu, Yueming Jin, Min Xu

Awards 

Best Paper Award

Predicting Falls through Muscle Weakness from a Single Whole Body Image: A Multimodal Contrastive Learning Framework

Xia Zhang, Afsah Saleem, Zaid Ilyas, David Suter, Uzair Nadeem, Richard L. Prince, Kun Zhu, Joshua R. Lewis, Marc Sim, and Syed Zulqarnain Gilani

Centre for AI and ML, School of Science, Edith Cowan University,  Australia

Nutrition and Health Innovation Research Institute, Edith Cowan University, Australia

Computer Science and Software Engineering, The University of Western Australia, Australia

Medical School, The University of Western Australia,  Australia

Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Perth, Western Australia,  Australia

Best Student Paper Award

Evaluating the Impact of Pulse Oximetry Bias in Machine Learning under Counterfactual Thinking

Inês Martins, João Matos, Tiago Gonçalves, Leo A. Celi, An-Kwok Ian Wong, and Jaime S. Cardoso

Faculty of Engineering, University of Porto

Institute for Systems and Computer Engineering, Technology and Science, Duke University

Massachusetts Institute of Technology

Best Student Paper Award – Honorable Mention

Evaluating Perceived Workload, Usability and Usefulness of Artificial Intelligence Systems in Low-Resource Settings: Semi-Automated Classification and Detection of Community Acquired Pneumonia   

Malaizyo G Muzumala, Ernest O Zulu, Peter Chibuta, Mayumbo Nyirenda, and Lighton Phiri

Department of Computer Science University of Zambia, Lusaka, Zambia

Department of Radiology, University Teaching Hospitals, Lusaka, Zambia

Department of Library and Information Science, University of Zambia, Lusaka, Zambia

Best Abstract Award

Automatic Vertebra Screw Planning in CT Throughout the Whole Spine

Alexandra Ertl, Paul Naser, Lisa Kausch, Peter Neher, Shuhan Xiao, Robin Peretzke, David Zimmerer, Markus Bujotzek, Moritz Scherer, Klaus Maier-Hein

Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany

Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, Germany

Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany

AI Health Innovation Cluster, German Cancer Research Center (DKFZ), Heidelberg, Germany

German Cancer Consortium (DKTK), partner site Heidelberg, Heidelberg, Germany

Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany

National Center for Tumor Diseases (NCT), NCT Heidelberg, Heidelberg, Germany 

Organizers and Sponsors

• • Shandong Wu, PhD, Associate Professor, University of Pittsburgh, USA; Email: wus3@upmc.edu 

  • Behrouz Shabestari, PhD, Director, National Technology Centers Program, National Institute of Biomedical Imaging and Bioengineering (NIBIB), USA; Email: behrouz.shabestari@nih.gov 

  • Lei Xing, PhD, Jacob Haimson & Sarah S. Donaldson Professor, Stanford University, USA; Email: lei@stanford.edu 

Sponsors

Pittsburgh Center for Artificial Intelligence Innovation in Medical Imaging

Previous Workshops

• The First Workshop on Applications of Medical AI (AMAI 2022),  September 18, 2022, Singapore

• The Second Workshop on Applications of Medical AI (AMAI 2023),  October 8, 2023, Vancouver, Canada