arXiv AI
Aug 5

A Deployment-Friendly Foundational Framework for Efficient Computational Pathology

arXiv:2602. 14010v2 Announce Type: replace-cross Abstract: Pathology foundation models (PFMs) generalize well across computational pathology tasks but remain costly for gigapixel whole-slide image analysis.

By Yu Cai, Cheng Jin, Zhengyu Zhang, Jiabo Ma, Fengtao Zhou, Yingxue Xu, Zhengrui Guo, Yihui Wang, Zhengyu Zhang, Ling Liang, Yonghao Tan, Pingcheng Dong, Du Cai, On Ki Tang, Chenglong Zhao, Zhijian Cen, Ying Tan, Xi Wang, Can Yang, Yali Xu, Jing Cui, Zhenhui Li, Ronald Cheong Kin Chan, Yueping Liu, Feng Gao, Xiuming Zhang, Li Liang, Hao Chen, Kwang-Ting Cheng
Hugging Face Trending Papers
Jul 9

Metrics or Mirage? An Audit of Evaluation Inconsistencies in Colonoscopy Polyp Segmentation Benchmarks

Progress in colonoscopy polyp segmentation is routinely reported through leaderboard comparisons on a small set of public benchmarks. We argue that this apparent progress is difficult to verify: a systematic audit of \textbf{27 papers} published between 2015 and 2026 reveals three structural problems in how the community evaluates models.

arXiv AI
Aug 20

A Few Cases Are All You Need: An Empirical Study of Annotation-Efficient LoRA Fine-Tuning of MedSAM3

The study investigates how few expert-annotated cases are needed to fine‑tune MedSAM3 for abdominal organ segmentation using Low‑Rank Adaptation (LoRA). With only 10 annotated CT or MRI cases, the LoRA‑adapted models achieve performance comparable to specialist systems that require orders of magnitude more data, including reliable gallbladder segmentation and near‑state‑of‑the‑art results for liver, kidneys, and spleen. The approach also generalizes to cardiac segmentation on the Whole Heart dataset, and training takes only 3–5 hours per organ on a single GPU, roughly twice as fast as nnU-Net.

By Sachin Dudda Nagaraju, Bendik Skarre Abrahamsen, Ashkan Moradi, Mattijs Elschot
arXiv Machine Learning
Sep 17

Democratizing Clinical Tumor Whole Genome Sequencing: 18-hour End-to-end Analysis via Trillion-parameter Large Language Models Locally Deployed on Consumer-grade Hardware

This paper reports a fully localized, low‑resource framework that runs a trillion‑parameter biomedical large language model on a single consumer‑grade RTX 4060 laptop (32 GB system memory, 8 GB VRAM) and routine clinical workstations. The system completes an entire tumor‑paired whole‑genome sequencing workflow—from raw FASTQ input to a clinical‑grade full‑variation‑spectrum report—in 18 hours at 30× depth, achieving a 99.62 % F1 score for somatic variant detection and over 99.9 % concordance with an industrial‑standard A100 cluster pipeline. The study demonstrates that adaptive heterogeneous memory scheduling accounts for 71 % of execution time and that model optimization adds less than 9 % of detection error, establishing a low‑cost, high‑accuracy pathway for global primary medical institutions to adopt precision oncology without expensive GPU clusters.

By Rui Xiao, Yili Xu
arXiv AI
Aug 11

Performance of large language models in the optical diagnosis of colorectal polyps

arXiv:2608. 07543v1 Announce Type: cross Abstract: Background and Study Aims: Accurate optical diagnosis of colorectal polyps guides resection strategy and surveillance, with multimodal large language models (MLLMs) showing potential for image-based diagnosis.

By Joshua C. Vences, William T. Tran, Nikko Gimpaya, Catharine M. Walsh, Rishad J. Khan, Robert Bechara, Asher C. Wiggins, Celine N. Rousan, Kaitlyn V. G. L. Morgado, Angie Ibrahim, Kevin H. M. Kuo, Daniel von Renteln, Alexander Hann, Dennis L. Shung, Michael A. Scaffidi, Charles M\'enard, Joshua Landy, Samir C. Grover