MSB Webinar: Panel Discussion on the Trips and Tips of MICCAI Review, Rebuttal, Challenge and Workshop

Sunday 17th March 2024

Are you curious about how you could craft a more compelling rebuttal? Ever thought about what it takes to clinch a win in a MICCAI challenge, or how to make your workshop submission stand out? The MICCAI Student Board brings you an unmissable opportunity to unravel these mysteries!

The MICCAI Student Board is happy to announce the first Panel Discussion in the MSB Webinar Series 2024. Join us on Friday, April 5th for an interactive discussion about the process, personal experiences, and suggestions for conducting the MICCAI paper review, rebuttal, and participating in the MICCAI challenges and workshops. This session aims to help students and early career researchers to get familiar with and prepare for the various submissions and academic activities associated with the MICCAI conference. So please mark your calendars for Friday, April 5th, 2024 and join us for this informative session.

The panel discussion includes the following parts covering topics on MICCAI paper review and rebuttal, MICCAI workshop, and MICCAI challenge.
1. Introducing the panelists [10min] 
2. Paper review and rebuttal at MICCAI + Q&A [30min] 
3. MICCAI Workshop paper submission + Q&A [20min] 
4. MICCAI Challenge participation + Q&A [20min]

Join us on Friday, April 5th
3:00 PM - 4:30 PM UTC / 8:00 AM - 9:30 AM PDT / 11:00 AM - 12:30 PM EDT

Registration (required) is free and open to everyone: Register here

About the Panelists:

We have six amazing panelists who are experienced in MICCAI paper review, rebuttal, MICCAI challenges, and workshop paper submissions. 

Chen (Cherise) Chen is a Lecturer (Assistant Professor) in Computer Vision, at the Department of Computer Science, University of Sheffield. Her main research interests lie in the interdisciplinary area of AI and healthcare, with a focus on robust medical image analysis. Since 2019, she has published ~40 papers in top conferences and journals on the topic of deep learning for medical data analysis, reaching 2,000+ Google Scholar citations with an h-index of 20.  Three out of her four first-authored MICCAI papers were selected to give oral presentations. Chen has also been awarded as a MICCAI outstanding reviewer in 2023 and the IEEE TMI Gold-level distinguished reviewer (2020-2022). She also served as the organizer of the MICCAI workshop on Data Augmentation, Labeling, and Imperfections (DALI), MICCAI 2023 and CMRxMotion challenge 2022; Program committee in ECCV-MCV; Reviewer of IEEE TMI, JBHI, MedIA, MICCAI, IPMI, MIDL, etc.

McKell Woodland is a PhD student in Computer Science at Rice University who researches at MD Anderson Cancer Center under Dr. Kristy Brock. Her research focuses on detecting when clinically-deployed segmentation models have performed poorly in order to protect patients with underrepresented characteristics from automation bias. As she works directly with segmentation models being employed in a clinical setting, efficiency and interpretability are a critical aspect of her out-of-distribution detection research. She has published papers in the SASHIMI and UNSURE MICCAI workshops, where she won a “Best Spotlight Paper” award for her work on improving the efficiency of the Mahalanobis distance for out-of-distribution detection.

Marawan Elbatel is a first-year PhD student at Hong Kong University of Science and Technology. He specializes in Medical Image Analysis through an Erasmus Mundus MSc spanning three universities in Europe after obtaining his BSc in Cairo University. He participated in-person at the last two MICCAI conferences, where he secured five satellite first-authored awards, including two challenge victories, one challenge runner-up and two best-paper awards in workshops.

Moucheng Xu is a research scientist in endoscopic vision at Odin Vision (an Olympus Corporation company). Previously, he obtained his PhD from the Centre for Medical Image Computing at University College London, funded by GSK and UCL Dean's Prize. His PhD focused on deep learning with limited labels for medical imaging. He was a finalist for the MICCAI Young Scientist Awards (Best Paper) in 2022. His other works have been presented in popular venues including MICCAI, MIDL, TMI, MeDIA, Nature, NeurIPS and BMVC. 

Harry Anthony is a PhD student at the University of Oxford, under the supervision of Professor Konstantinos Kamnitsas. Harry’s research focuses on improving the reliability of deep neural networks in the field of medical imaging, with a particular emphasis on out-of-distribution (OOD) detection. Harry’s research aims to develop novel methods for deep learning algorithms to detect OOD inputs, as well as improving the overall performance of medical imaging analysis. Prior to his doctoral studies, Harry obtained a first class master’s degree in Physics from Imperial College London.

John Kalkhof is a PhD student at TU Darmstadt and part of the MEC-Lab under Anirban Mukhopadhyay's guidance, is committed to advancing affordable healthcare AI to expand access to care. Initially exploring feature disentanglement during his Master's, his current research focuses on lightweight and robust segmentation methods through Neural Cellular Automata. His work was recognized at IPMI 2023 with the Francois Erbsmann Prize and 2023 with the MICCAI Young Scientist Award.