Machine learning methods for histopathological image analysis: A review
Histopathological images (HIs) are the gold standard for evaluating some types of tumors for
cancer diagnosis. The analysis of such images is time and resource-consuming and very …
cancer diagnosis. The analysis of such images is time and resource-consuming and very …
[HTML][HTML] Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey
Digital pathology technologies, including whole slide imaging (WSI), have significantly
improved modern clinical practices by facilitating storing, viewing, processing, and sharing …
improved modern clinical practices by facilitating storing, viewing, processing, and sharing …
[HTML][HTML] Yottixel–an image search engine for large archives of histopathology whole slide images
S Kalra, HR Tizhoosh, C Choi, S Shah… - Medical Image …, 2020 - Elsevier
With the emergence of digital pathology, searching for similar images in large archives has
gained considerable attention. Image retrieval can provide pathologists with unprecedented …
gained considerable attention. Image retrieval can provide pathologists with unprecedented …
A comparative study of CNN, BoVW and LBP for classification of histopathological images
Despite the progress made in the field of medical imaging, it remains a large area of open
research, especially due to the variety of imaging modalities and disease-specific …
research, especially due to the variety of imaging modalities and disease-specific …
Histopathology image classification using bag of features and kernel functions
Image representation is an important issue for medical image analysis, classification and
retrieval. Recently, the bag of features approach has been proposed to classify natural …
retrieval. Recently, the bag of features approach has been proposed to classify natural …
An unsupervised feature learning framework for basal cell carcinoma image analysis
Objective The paper addresses the problem of automatic detection of basal cell carcinoma
(BCC) in histopathology images. In particular, it proposes a framework to both, learn the …
(BCC) in histopathology images. In particular, it proposes a framework to both, learn the …
Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval
Content-based histopathological image retrieval (CBHIR) has become popular in recent
years in histopathological image analysis. CBHIR systems provide auxiliary diagnosis …
years in histopathological image analysis. CBHIR systems provide auxiliary diagnosis …
Heterogeneity-aware local binary patterns for retrieval of histopathology images
Histopathology images exhibit considerable variability, which can make diagnosis prone to
uncertainty and errors. Using retrieval systems to locate similar images when a query image …
uncertainty and errors. Using retrieval systems to locate similar images when a query image …
Using DICOM metadata for radiological image series categorization: a feasibility study on large clinical brain MRI datasets
The growing interest in machine learning (ML) in healthcare is driven by the promise of
improved patient care. However, how many ML algorithms are currently being used in …
improved patient care. However, how many ML algorithms are currently being used in …
Size-scalable content-based histopathological image retrieval from database that consists of WSIs
Content-based image retrieval (CBIR) has been widely researched for histopathological
images. It is challenging to retrieve contently similar regions from histopathological whole …
images. It is challenging to retrieve contently similar regions from histopathological whole …