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…
Unrolled Networks are Conditional Probability Flows in MRI Reconstruction
Kehan Qi, Saumya Gupta, Xiaoling Hu +3
Unrolled networks have been widely used for Magnetic Resonance Imaging (MRI) reconstruction due to their efficiency. However, they typically exhibit unstable output quality across…
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…