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Found 10 papers, 0 papers with code
SPLICE -- Streamlining Digital Pathology Image Processing
As an unsupervised method, SPLICE effectively reduces storage requirements for representing tissue images by 50%.
Analysis and Validation of Image Search Engines in Histopathology
Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and diagnosis to prognosis and prediction.
Selection of Distinct Morphologies to Divide & Conquer Gigapixel Pathology Images
We assess the representativeness of the SDM montage across various public and private histopathology datasets.
Rotation-Agnostic Image Representation Learning for Digital Pathology
This paper addresses complex challenges in histopathological image analysis through three key contributions.
Breast Cancer Histology Image Classification
Medical Image Retrieval
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When is a Foundation Model a Foundation Model
Recently, several studies have reported on the fine-tuning of foundation models for image-text modeling in the field of medicine, utilizing images from online data sources such as Twitter and PubMed.
A Preliminary Investigation into Search and Matching for Tumour Discrimination in WHO Breast Taxonomy Using Deep Networks
Searchable digital atlases can provide pathologists with patch matching tools allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion.
Immunohistochemistry Biomarkers-Guided Image Search for Histopathology
In this work, we are proposing a targeted image search approach, inspired by the pathologists workflow, which may use information from multiple IHC biomarker images when available.
Composite Biomarker Image for Advanced Visualization in Histopathology
In the first step, IHC biomarker images are aligned with the H&E images using one coordinate system and orientation.
Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'
Chen et al. [Chen2022] recently published the article 'Fast and scalable search of whole-slide images via self-supervised deep learning' in Nature Biomedical Engineering.
Automatic Multi-Stain Registration of Whole Slide Images in Histopathology
The registration was performed automatically by first detecting landmarks in both images, using the scale-invariant image transform (SIFT), followed by the fast sample consensus (FSC) protocol for finding point correspondences and finally aligned the images.
