The paper introduces SlideTIM, a transductive few‑shot classification method tailored for whole‑slide images (WSIs). SlideTIM extends the LC‑TIM approach by adding a spatial‑latent regularizer and a class‑distribution prior, ensuring that spatially and semantically similar patches receive consistent predictions and that predicted class proportions are calibrated. Experiments on four histology datasets show that SlideTIM outperforms existing TIM variants, boosting macro‑F1 scores by up to 8.1 percentage points over the best baseline and 19.4 percentage points over zero‑shot predictions at one shot.
By Tiffanie Godelaine, Manon Dausort, Karim El Khoury, Beno\^it G\'erin, Beno\^it Macq, Christophe De Vleeschouwer
LanGuSTE is a patch‑selection framework for whole slide image analysis that uses vision‑language models and large language model knowledge. It introduces Cross‑Scale Visual Prompt Tuning to align low‑resolution and high‑resolution patches, and a coarse‑to‑fine selection module that encodes only informative high‑resolution patches. Experiments show LanGuSTE cuts overall processing time to about one‑third of the baseline while matching or surpassing diagnostic performance of exhaustive and state‑of‑the‑art methods.
By Yonghan Shin, Gangsu Kim, Won-Ki Jeong
arXiv:2609.00396v1 Announce Type: new
Abstract: Histopathological whole slide images (WSIs) are central to cancer diagnosis, but their gigapixel scale, tissue heterogeneity, weak slide-level supervis...
By Chad Wong, Sicheng Chen, Tianyi Zhang, Enhui Chai, Yueming Jin, Zeyu Liu, Fei Xia
The paper introduces a pipeline that uses publicly available whole slide image foundation models (FMs) to automatically triage slides by ranking them based on zero‑shot classification predictions. This approach accurately identifies slides containing the most tumor, achieving top‑2 ranking for patients with up to 43 slides across multiple datasets. The study also proposes a ranked evaluation framework to benchmark FM performance in slide triage.
By Ayushi Sinha, Shashank Yadav, Benjamin Holmes, Pravat Das, Aaron W. Bogan, James S. Lewis Jr., Santiago Romero-Brufau, Andrew Y. K. Foong, Scott H. Kaufmann, Kathryn M. Van Abel, David M. Routman, Michael R. Lucas
The paper introduces Locked Image Multiple Instance PreTraining (LIMIT), a method that aligns slide‑level pathology reports with patch‑level visual features by freezing a vision encoder and fine‑tuning a text encoder. LIMIT uses report embeddings as cross‑attention queries over bags of patch features, optimizing slide representations via InfoNCE. Applying LIMIT, the authors create CONCH‑Z, which achieves state‑of‑the‑art zero‑shot performance on 22 pathology tasks, outperforming vision‑language encoders trained on curated caption datasets and narrowing the gap with larger slide‑level foundation models.
By Daniel Shao, Dongmin Bang, Luca L. Weishaupt, Sophia J. Wagner, Nic G. Reitsam, Ming Y. Lu, Long P. Le, Richard J. Chen, Faisal Mahmood
arXiv:2610.15669v2 Announce Type: replace-cross
Abstract: Weak bag-level labels are widely available in computational pathology, whereas reliable instance-level annotations for whole-slide images rem...
By Chen Shu, Chengyang Zhang, Boyu Fu, Ting Yin, Wenchuan Zhang, Jie Chen, Yuhao Yi, Hong Bu
arXiv:2510.06113v2 Announce Type: replace
Abstract: Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which red...
By Shuo Jiang, Zhuwen Chen, Liaoman Xu, Yanming Zhu, Changmiao Wang, Jiong Zhang, Feiwei Qin, Yifei Chen, Zhu Zhu
arXiv:2607.19261v4 Announce Type: replace-cross
Abstract: Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating...
By Dankai Liao, Tianyi Zhang, Yufeng Wu, Xinyue Zhang, Qiaochu Xue, Zeyu Liu, Dachun Zhao, Linghan Cai, Yueming Jin
SlideBank is a training‑free framework that turns each whole‑slide image into a persistent, concept‑indexed evidence bank. It performs coarse‑to‑fine exploration to locate informative regions and multi‑scale views, converts them into explicit morphological observations, and anchors pathology signals to the supporting patches and slide coordinates. During inference, questions are routed to relevant signals and evidence scales, and a confidence‑based cross‑level consensus integrates global, regional, and patch evidence, achieving high accuracy on WSI‑VQA and SlideBench‑BCNB while enabling consistent re‑phrasing and reduced inference cost.
By Beidi Zhao, Gexin Huang, Ciro Zhang, Anqi Li, Yusheng Tan, Chen Zhou, Gang Wang, Zu-hua Gao, Xiaoxiao Li
arXiv:2607. 12278v1 Announce Type: cross Abstract: Recent vision-language models (VLMs) for computational pathology report striking zero-shot performance on whole-slide image (WSI) visual question answering (VQA) benchmarks.
By Wenhao Zhang, Zhongliang Zhou, John Kang, Sheng Li
arXiv:2606. 07590v1 Announce Type: cross Abstract: Pathology foundation models are pretrained on large streams of WSI-derived patches, while supervision during data construction is often slide-level, sparse, or heterogeneous.
By Mingyi He, Xinyi Guo, Xitong Ling, Weiming Chen, Jiawen Li, Lianghui Zhu, Minxi Ouyang, Mingxi Fu, Yizhi Wang, Tian Guan
arXiv:2606. 06864v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become a standard paradigm for whole slide image (WSI) analysis in digital pathology, as it enables slide-level prediction without dense annotations.
By Yonghan Shin, Won-Ki Jeong