Potential Pitfalls of Deep Learning in Medical Image Reconstruction


This session will focus on scenarios in which deep learning algorithms developed for medical imaging might produce unreliable results, e.g. due to distribution shifts, bias, hallucinations, or other factors. The session is planned following the increasing interest in studying sensitivities and instabilities of such algorithms. The talks will discuss strategies for exposing algorithmic sensitivities and addressing them, preventing inverse crimes, and increasing algorithmic interpretability. The aim of the session is to raise awareness to the growing problem of unreliable AI performance in the context of medical imaging, suggest guidelines and solutions, and invoke community discussions.

Organisers: Efrat Shimron (Chair) & Florian Knoll (Committee member)

Session Schedule

17:30 - 17:50 Invited talk -
The Risk in Naive Training of Medical AI Algorithms: Pitfalls, Stability and Generalizability Issues
Efrat Shimron
17:50 - 18:10 Invited talk -
Trustworthy Image Reconstruction and beyond: Using Adversarial Approaches
Daniel Rueckert
18:10 - 18:30 Invited talk -
Generalizability (or not?) of patch-based image models
Jeffrey Fessler
18:30 - 18:50 Invited talk -
The importance of signal modeling for MRI artifact mitigation
Jon Tamir
18:50 - 19:10 Invited talk -
Generalised hardness of approximation and hallucinations -- On barriers and paradoxes in image reconstruction
Anders Hansen
19:10 - 20:30 Invited poster -
Mitigating Data Paucity and Distributional Shifts for Accelerated MRI Alongside New Clinically-Relevant Evaluation Metrics
Akshay S. Chaudhari
19:10 - 20:30 Contributed poster -
Physics-Driven Data Priors for Robust Self-Supervised Accelerated MRI Reconstruction
Arjun Desai*
19:10 - 20:30 Contributed poster -
MRI Reconstruction via Data-Driven Markov Chains with Joint Uncertainty Estimation
Guanxiong Luo*
19:10 - 20:30 Contributed poster -
Partial privacy loss in machine learning: a statistical signal processing perspective
Tamara T. Mueller*
19:10 - 20:30 Committee member poster -
Insights into the reliability of deep learning reconstructions with research challenges
Florian Knoll