6 papers
LoC-Path: Learning to Compress for Pathology Multimodal Large Language Models
Qingqiao Hu, Weimin Lyu, Meilong Xu +5
Whole Slide Image (WSI) MLLMs are difficult to build and deploy because gigapixel slides induce thousands of visual tokens, while only a small fraction of regions is diagnostically…
FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstruction
Kehan Qi, Saumya Gupta, Xiaoling Hu +4
Unrolled networks are widely used in Magnetic Resonance Imaging (MRI) reconstruction for their efficiency. Structured as a series of neural network stages (or cascades), an unrolle…
Efficient Whole Slide Pathology VQA via Token Compression
Weimin Lyu, Qingqiao Hu, Kehan Qi +4
Whole-slide images (WSIs) in pathology can reach up to 10,000 x 10,000 pixels, posing significant challenges for multimodal large language model (MLLM) due to long context length a…
BrainMRDiff: A Diffusion Model for Anatomically Consistent Brain MRI Synthesis
Moinak Bhattacharya, Saumya Gupta, Annie Singh +3
Accurate brain tumor diagnosis relies on the assessment of multiple Magnetic Resonance Imaging (MRI) sequences. However, in clinical practice, the acquisition of certain sequences…
TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model
Meilong Xu, Saumya Gupta, Xiaoling Hu +5
Accurately modeling multi-class cell topology is crucial in digital pathology, as it provides critical insights into tissue structure and pathology. The synthetic generation of cel…
TopoDiffusionNet: A Topology-aware Diffusion Model
Saumya Gupta, Dimitris Samaras, Chao Chen
Diffusion models excel at creating visually impressive images but often struggle to generate images with a specified topology. The Betti number, which represents the number of stru…