9 papers
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis
Jian-Qing Zheng, Yuanhan Mo, Yang Sun +5
In medical imaging, the diffusion models have shown great potential for synthetic image generation tasks. However, these approaches often lack the interpretable connections between…
Representation Invariance and Allocation: When Subgroup Balance Matters
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…
SpineFM: Leveraging Foundation Models for Automatic Spine X-ray Segmentation
Samuel J. Simons, BartÅomiej W. Papież
This paper introduces SpineFM, a novel pipeline that achieves state-of-the-art performance in the automatic segmentation and identification of vertebral bodies in cervical and lumb…