arXiv AI
ADMIL: Attention-Distilled Multiple Instance Learning for Selective Foundation Model Inference in Pathology
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arXiv AI
Jul 17
Pretraining Multiple Instance Learning Networks with Multi-Teacher Distillation from Pathology Slide Foundation Models
arXiv:2607. 14703v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become the main paradigm for whole-slide image (WSI) analysis in computational pathology.
arXiv Machine Learning
Jun 8
LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification
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.
arXiv Computer Vision
Aug 25
LanGuSTE: Language-Guided Coarse-to-Fine Patch Selection for Efficient Whole Slide Image Analysis
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.
arXiv Computer Vision
Sep 2
SlideMix: Enhancing Whole Slide Image Analysis via Multimodal Shuffling
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...
arXiv Computer Vision
Oct 12
Scaling Multi-Teacher Distillation for Digital Pathology
The paper introduces a scalable multi-teacher distillation (MTD) framework for digital pathology, addressing the poor performance of standard MTD when many teachers are used. By learning teacher‑specific representations through techniques such as learnable teacher tokens, a tailored attention scheme, mixture‑of‑experts layers, and a contrastive loss, the method scales effectively, enabling compact student encoders that are up to 10× more efficient than larger teacher models while matching or surpassing their performance on 39 tile‑ and slide‑level tasks. The authors release a new collection of efficient foundation models, OMNI, trained from ten state‑of‑the‑art teachers and provide an analysis of the complementary teacher‑specific representations.
arXiv Computer Vision
Oct 16
Beyond Sparse Captions: Aligning Slide-Level Text and Patch-Level Vision in Pathology
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.