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Workshop on Machine Intelligence in Medical Image Computing

Flemingsberg, 3rd of October 2024

Welcome to the Workshop on Machine Intelligence in Medical Image Computing organized by the Division of Biomedical Imaging at the Department of Biomedical Engineering and Health Systems at KTH Royal Institute of Technology.

Time: Oct 3, 14:00-17:30

Location: T55, KTH Flemingsberg or on Zoom: kth-se.zoom.us/j/63524448317

Registration:  www.kth.se/form/66f17204f51474b07bcdaa46

Program:

14:00-14:30 Keynote: Interpretable AI for medical image analysis Prof. Xiahai Zhuang, Fudan University, China

14:30-14:50 AnatomyArchive: knowledge-embedded 3D Anatomy Representation, Image Feature Exploration and Visualization for CT-based Clinical Studies Dr. Lei Xu, KI/ CLINTEC

14:50-15:10 Oncological image analysis with machine learning methods Dr. Mehdi Astaraki, Stockholm University

15:10-15:30 From pixels to predictions: Radiomics and its application in liver cancer management Dr. Qiang Wang, KI/ CLINTEC

15:30-15:50 Coffee break

15:50-16:10 The desiderata of continual medical image segmentation: beyond defying forgetting Hangqi Zhou, Fudan University, China

16:10-16:30 Geometric Deep Learning in Medical Image Processing Fabian Sinzinger, KTH/MTH

16:30-16:50 Advancing PET-CT Lymphoma Lesion Detection and Classification: From State-of-the-Art Methods to Federated Learning for Enhanced Generalizability Simone Bendazzoli, KTH/MTH

16:50-17:10 Randomly COMMITing: Filtering and aggregation of Tractogram subsets Sanna Persson, KTH/MTH

17:10-17:30 Deep Leaning based Breast Radiological Image Analysis Zhikai Yang, KTH/MTH

Keynote speaker: Prof. Xiahai Zhuang is a professor at the school of data science of Fudan University, China. He obtained my PhD from the Centre for Medical Image Computing, University College London. His research interests include artificial intelligence, statistical learning, medical imaging, bio-medical & healthcare engineering, particularly on the topics of interpretable AI and explainable AI for Medical image analysis. He serves as editorial board member for IEEE TMI, Med Image Anal, Neural Networks, Neurocomputing, Mathematics. He is elected Board Member (2022-2026), and Executive Director (2024-2027) of the MICCAI Society. His interpretable Bayesian segmentation deep learning work won the Elsevier-MedIA 1st Prize and Medical Image Analysis MICCAI Best Paper Award 2023.