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cs.CV2025
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…
cs.CV2025
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…
cs.CV2025
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…