Breast cancer histopathology image analysis: A review
This paper presents an overview of methods that have been proposed for the analysis of
breast cancer histopathology images. This research area has become particularly relevant …
breast cancer histopathology images. This research area has become particularly relevant …
The devil is in the details: Whole slide image acquisition and processing for artifacts detection, color variation, and data augmentation: A review
Whole Slide Images (WSI) are widely used in histopathology for research and the diagnosis
of different types of cancer. The preparation and digitization of histological tissues leads to …
of different types of cancer. The preparation and digitization of histological tissues leads to …
Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks
Generalizability of algorithms for binary cancer vs. no cancer classification is unknown for
clinically more significant multi-class scenarios where intermediate categories have different …
clinically more significant multi-class scenarios where intermediate categories have different …
Histosegnet: Semantic segmentation of histological tissue type in whole slide images
L Chan, MS Hosseini, C Rowsell… - Proceedings of the …, 2019 - openaccess.thecvf.com
In digital pathology, tissue slides are scanned into Whole Slide Images (WSI) and
pathologists first screen for diagnostically-relevant Regions of Interest (ROIs) before …
pathologists first screen for diagnostically-relevant Regions of Interest (ROIs) before …
Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images
Selecting regions of interest (ROI) is a common step in medical image analysis across all
imaging modalities. An ROI is a subset of an image appropriate for the intended analysis …
imaging modalities. An ROI is a subset of an image appropriate for the intended analysis …
Classification of breast cancer from histopathology images using an ensemble of deep multiscale networks
Manual delineation of tumours in breast histopathology images is generally time-consuming
and laborious. Computer-aided detection systems can assist pathologists by detecting …
and laborious. Computer-aided detection systems can assist pathologists by detecting …
Breast cancer histopathological image classification: is magnification important?
Breast cancer is one of the most common cancer in women worldwide. It is typically
diagnosed via histopathological microscopy imaging, for which image analysis can aid …
diagnosed via histopathological microscopy imaging, for which image analysis can aid …
Fundamental developments in infrared spectroscopic imaging for biomedical applications
M Pilling, P Gardner - Chemical Society reviews, 2016 - pubs.rsc.org
Infrared chemical imaging is a rapidly emerging field with new advances in instrumentation,
data acquisition and data analysis. These developments have had significant impact in …
data acquisition and data analysis. These developments have had significant impact in …
Attention by selection: A deep selective attention approach to breast cancer classification
Deep learning approaches are widely applied to histopathological image analysis due to the
impressive levels of performance achieved. However, when dealing with high-resolution …
impressive levels of performance achieved. However, when dealing with high-resolution …
Infrared spectral histopathology (SHP): a novel diagnostic tool for the accurate classification of lung cancer
B Bird, M Miljković, S Remiszewski, A Akalin… - Laboratory …, 2012 - nature.com
We report results of a study utilizing a recently developed tissue diagnostic method, based
on label-free spectral techniques, for the classification of lung cancer histopathological …
on label-free spectral techniques, for the classification of lung cancer histopathological …