ArticleNPJ digital medicine2019
Similar image search for histopathology: SMILY.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning.Sensors (Basel, Switzerland) · 2021Pooled it
- Generalist foundation models from a multimodal dataset for 3D computed tomography.Nature biomedical engineering · 2026Article
- Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspective.Bioengineering (Basel, Switzerland) · 2026Review
- Adaptive example selection for prototype based explainable mitosis detection in digital pathology.Scientific reports · 2026Article
- Mind the gap: challenges and future directions for content-based image retrieval in clinical radiology.Frontiers in radiology · 2026Article
- MuCoSA: Multi-contextual similarity assessment for histopathology image search.Journal of pathology informatics · 2026Article
- Artificial intelligence and robotic surgery in clinical medicine: progress, challenges, and future directions.Future science OA · 2025Review
- Quantification-based explainable artificial intelligence for deep learning decisions: clustering and visualization of quantitative morphometric features in hepatocellular carcinoma discrimination.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- 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
- A visual-omics foundation model to bridge histopathology with spatial transcriptomics.Nature methods · 2025Article
- Content-Based Histopathological Image Retrieval.Sensors (Basel, Switzerland) · 2025Article
- On image search in histopathology.Journal of pathology informatics · 2024Review
- Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma.Journal of pathology informatics · 2024Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Cross-modality sub-image retrieval using contrastive multimodal image representations.Scientific reports · 2024Article
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- A visual-language foundation model for computational pathology.Nature medicine · 2024Article
- Leveraging physiology and artificial intelligence to deliver advancements in health care.Physiological reviews · 2023Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Although these datasets typically have associated metadata such as diagnosis or clinical notes, even carefully curated datasets rarely contain annotations of the location of regions of interest on each image. As pathology images are extremely large (up to 100,000 pixels in each dimension), further laborious visual search of each image may be needed to find the feature of interest. In this paper, we introduce a deep-learning-based reverse image search tool for histopathology images: Similar Medical Images Like Yours (SMILY). We assessed SMILY's ability to retrieve search results in two ways: using pathologist-provided annotations, and via prospective studies where pathologists evaluated the quality of SMILY search results. As a negative control in the second evaluation, pathologists were blinded to whether search results were retrieved by SMILY or randomly. In both types of assessments, SMILY was able to retrieve search results with similar histologic features, organ site, and prostate cancer Gleason grade compared with the original query. SMILY may be a useful general-purpose tool in the pathologist's arsenal, to improve the efficiency of searching large archives of histopathology images, without the need to develop and implement specific tools for each application.
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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 OpenQuestion graph.