ArticleNature biomedical engineering2022
Fast and scalable search of whole-slide images via self-supervised deep learning.
Article in Nature biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- From traditional histopathology to virtual staining: deep learning approaches for assessing tissue lesions in farmed fish.Veterinary research communications · 2026Review
- DFCE-KanT: predicting spatial gene expression from histology images via contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- Generalist foundation models from a multimodal dataset for 3D computed tomography.Nature biomedical engineering · 2026Article
- Spatially resolved tissue architecture and computational pathology in pancreatic cancer.Experimental & molecular medicine · 2026Review
- Trimodal, uncertainty-guided whole-slide framework for genome-scale spatial expression and image-only virtual perturbation in cancer cohorts.Genome medicine · 2026Article
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- mViSE: A visual search engine for analyzing multiplex IHC brain tissue images (spatial proteomics).Scientific reports · 2026Article
- A Generative Foundation Model for Scalable Cytology Image Synthesis in AI-Powered Diagnostics.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Application of hierarchical self-supervised contrastive learning in domain adaptation matching of multimodal remote sensing image.Scientific reports · 2026Article
- Explainable deep learning approaches and clinical insights for cancer biomarker identification.Frontiers in oncology · 2026Review
- Article
- MuCoSA: Multi-contextual similarity assessment for histopathology image search.Journal of pathology informatics · 2026Article
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
- Web engine for tumor pathology image retrievals on massive scales.bioRxiv : the preprint server for biology · 2025Article
- Contrastive learning uncovers cellular interactions and morphologies in the tumor microenvironment of lung adenocarcinoma linked to immunotherapy response.Scientific reports · 2025Article
- Digital Pathology and Artificial Intelligence Applied to Nonclinical Toxicology Pathology-The Current State, Challenges, and Future Directions.Toxicologic pathology · 2025Review
- A visual-omics foundation model to bridge histopathology with spatial transcriptomics.Nature methods · 2025Article
- A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images.Biomedical engineering online · 2025Article
- Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers.BMC medical imaging · 2025Article
Corrections and comments
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6 authors.
Funding
Abstract
The adoption of digital pathology has enabled the curation of large repositories of gigapixel whole-slide images (WSIs). Computationally identifying WSIs with similar morphologic features within large repositories without requiring supervised training can have significant applications. However, the retrieval speeds of algorithms for searching similar WSIs often scale with the repository size, which limits their clinical and research potential. Here we show that self-supervised deep learning can be leveraged to search for and retrieve WSIs at speeds that are independent of repository size. The algorithm, which we named SISH (for self-supervised image search for histology) and provide as an open-source package, requires only slide-level annotations for training, encodes WSIs into meaningful discrete latent representations and leverages a tree data structure for fast searching followed by an uncertainty-based ranking algorithm for WSI retrieval. We evaluated SISH on multiple tasks (including retrieval tasks based on tissue-patch queries) and on datasets spanning over 22,000 patient cases and 56 disease subtypes. SISH can also be used to aid the diagnosis of rare cancer types for which the number of available WSIs is often insufficient to train supervised deep-learning models.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.