31 citations · 92 across the 21 of their papers we have counts for
5 papers · 1 filter
When does Subgroup Balance Matter? Predicting Performance Sensitivity from Latent Separability
Anissa Alloula, Charles Jones, Zuzanna Wakefield-Skorniewska +2
Unequal representation of demographic groups in training data poses challenges to model generalisation across populations. Standard practice assumes that balancing subgroup represe…
Emerging Semantic Segmentation from Positive and Negative Coarse Label Learning
Le Zhang, Fuping Wu, Arun Thirunavukarasu +3
Large annotated datasets are vital for training segmentation models, but pixel-level labeling is time-consuming, error-prone, and often requires scarce expert annotators, especiall…
Interpretable Rheumatoid Arthritis Scoring via Anatomy-aware Multiple Instance Learning
Zhiyan Bo, Laura C. Coates, Bartlomiej W. Papiez
The Sharp/van der Heijde (SvdH) score has been widely used in clinical trials to quantify radiographic damage in Rheumatoid Arthritis (RA), but its complexity has limited its adopt…
Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
Zuzanna Skorniewska, Bartlomiej W. Papiez
The adoption of neural network models in medical imaging has been constrained by strict privacy regulations, limited data availability, high acquisition costs, and demographic bias…
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST
Fuping Wu, Bartlomiej W. Papiez
Foundation models are widely employed in medical image analysis, due to their high adaptability and generalizability for downstream tasks. With the increasing number of foundation…