Clinically conditioned medical image generation

The problem

Development and validation of a model to produce anatomically plausible medical images consistent with clinically relevant disease and treatment conditions.

What you will do

As in every MSc thesis in the BEAR group, you will:

• develop a novel module, neural architecture or learning strategy that addresses an open challenge in medical imaging (not a plain replication of existing methods);
• validate it in one or more clinically relevant application domains, on established medical imaging benchmarks.

Possible application domains

Radiology (miscellaneous) · Neuro-radiology · Neuro-oncology · Ophthalmology

Example to follow

Modality-Aware Token Interaction (MATI): a module that structures multimodal interaction within a single token stream by partitioning the embedding space into modality-aligned subspaces, integrated into a neural backbone (e.g. UNETR) and validated on an established benchmark (e.g. BraTS for multimodal brain tumour segmentation), with optional generalisation to more backbones and benchmarks.