scholarly journals Interactive thyroid whole slide image diagnostic system using deep representation

2020 ◽  
Vol 195 ◽  
pp. 105630
Author(s):  
Pingjun Chen ◽  
Xiaoshuang Shi ◽  
Yun Liang ◽  
Yuan Li ◽  
Lin Yang ◽  
...  
Keyword(s):  
Diagnostic System ◽  
Whole Slide Image ◽  
Slide Image

scholarly journals Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shan Guleria ◽  
Tilak U. Shah ◽  
J. Vincent Pulido ◽  
Matthew Fasullo ◽  
Lubaina Ehsan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Accuracy ◽  
High Sensitivity ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Learning Approaches ◽  
Learning Models ◽  
Whole Slide Image ◽  
Slide Image ◽  
Level Model

AbstractProbe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett’s esophagus (BE) but is limited by low sensitivity. Even the gold standard of histopathology is hindered by poor agreement between pathologists. We deployed deep-learning-based image and video analysis in order to improve diagnostic accuracy of pCLE videos and biopsy images. Blinded experts categorized biopsies and pCLE videos as squamous, non-dysplastic BE, or dysplasia/cancer, and deep learning models were trained to classify the data into these three categories. Biopsy classification was conducted using two distinct approaches—a patch-level model and a whole-slide-image-level model. Gradient-weighted class activation maps (Grad-CAMs) were extracted from pCLE and biopsy models in order to determine tissue structures deemed relevant by the models. 1970 pCLE videos, 897,931 biopsy patches, and 387 whole-slide images were used to train, test, and validate the models. In pCLE analysis, models achieved a high sensitivity for dysplasia (71%) and an overall accuracy of 90% for all classes. For biopsies at the patch level, the model achieved a sensitivity of 72% for dysplasia and an overall accuracy of 90%. The whole-slide-image-level model achieved a sensitivity of 90% for dysplasia and 94% overall accuracy. Grad-CAMs for all models showed activation in medically relevant tissue regions. Our deep learning models achieved high diagnostic accuracy for both pCLE-based and histopathologic diagnosis of esophageal dysplasia and its precursors, similar to human accuracy in prior studies. These machine learning approaches may improve accuracy and efficiency of current screening protocols.

scholarly journals Whole Slide Image Classification via Iterative Patch Labelling

2018 ◽  
Author(s):  
Chaoyi Zhang ◽  
Yang Song ◽  
Donghao Zhang ◽  
Sidong Liu ◽  
Mei Chen ◽  
...  
Keyword(s):  
Image Classification ◽  
Whole Slide Image ◽  
Slide Image

scholarly journals Robust whole slide image analysis for cervical cancer screening using deep learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shenghua Cheng ◽  
Sibo Liu ◽  
Jingya Yu ◽  
Gong Rao ◽  
Yuwei Xiao ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Screening ◽  
Cervical Cancer Screening ◽  
True Positive Rate ◽  
Classification Model ◽  
Computer Assisted ◽  
Whole Slide Image ◽  
Slide Image ◽  
Positive Rate ◽  
Analysis System

AbstractComputer-assisted diagnosis is key for scaling up cervical cancer screening. However, current recognition algorithms perform poorly on whole slide image (WSI) analysis, fail to generalize for diverse staining and imaging, and show sub-optimal clinical-level verification. Here, we develop a progressive lesion cell recognition method combining low- and high-resolution WSIs to recommend lesion cells and a recurrent neural network-based WSI classification model to evaluate the lesion degree of WSIs. We train and validate our WSI analysis system on 3,545 patient-wise WSIs with 79,911 annotations from multiple hospitals and several imaging instruments. On multi-center independent test sets of 1,170 patient-wise WSIs, we achieve 93.5% Specificity and 95.1% Sensitivity for classifying slides, comparing favourably to the average performance of three independent cytopathologists, and obtain 88.5% true positive rate for highlighting the top 10 lesion cells on 447 positive slides. After deployment, our system recognizes a one giga-pixel WSI in about 1.5 min.

scholarly journals Validation of an image analysis algorithm to quantify leukocyte populations using whole slide image analysis

2013 ◽  
Vol 1 (S1) ◽  
Author(s):  
Anthony J Milici ◽  
David Young ◽  
Steven J Potts ◽  
Holger Lange ◽  
Nicholas D Landis ◽  
...  
Keyword(s):  
Image Analysis ◽  
Analysis Algorithm ◽  
Whole Slide Image ◽  
Image Analysis Algorithm ◽  
Slide Image ◽  
Leukocyte Populations

A Brief Review of Computer-aided Whole-slide Image Detection Techniques

2020 ◽  
Author(s):  
Xintong Li ◽  
Chen Li ◽  
Xiaoqi Li ◽  
Jian Wu ◽  
Xiaoyan Li
Keyword(s):  
Image Detection ◽  
Whole Slide Image ◽  
Detection Techniques ◽  
Computer Aided ◽  
Slide Image
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