Deep neural network models for computational histopathology: A survey
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Weakly supervised deep learning for whole slide lung cancer image analysis
Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient
and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients …
and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients …
A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer
C Ho, Z Zhao, XF Chen, J Sauer, SA Saraf… - Scientific reports, 2022 - nature.com
Colorectal cancer is one of the most common cancers worldwide, accounting for an annual
estimated 1.8 million incident cases. With the increasing number of colonoscopies being …
estimated 1.8 million incident cases. With the increasing number of colonoscopies being …
MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images
The analysis of glandular morphology within colon histopathology images is an important
step in determining the grade of colon cancer. Despite the importance of this task, manual …
step in determining the grade of colon cancer. Despite the importance of this task, manual …
A comprehensive review for breast histopathology image analysis using classical and deep neural networks
Breast cancer is one of the most common and deadliest cancers among women. Since
histopathological images contain sufficient phenotypic information, they play an …
histopathological images contain sufficient phenotypic information, they play an …
Attention-enriched deep learning model for breast tumor segmentation in ultrasound images
Incorporating human domain knowledge for breast tumor diagnosis is challenging because
shape, boundary, curvature, intensity or other common medical priors vary significantly …
shape, boundary, curvature, intensity or other common medical priors vary significantly …
RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis.
However, due to the large scale of WSIs and various sizes of the abnormal area, how to …
However, due to the large scale of WSIs and various sizes of the abnormal area, how to …
Histosegnet: Semantic segmentation of histological tissue type in whole slide images
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 …
A comprehensive analysis of weakly-supervised semantic segmentation in different image domains
Recently proposed methods for weakly-supervised semantic segmentation have achieved
impressive performance in predicting pixel classes despite being trained with only image …
impressive performance in predicting pixel classes despite being trained with only image …