Deep Learning for Prostate Pathology

10/11/2019

∙

The current study detects different morphologies related to prostate pathology using deep learning models; these models were evaluated on 2,121 hematoxylin and eosin (H E) stain histology images that spanned a variety of image qualities, origins (Whole-slide, Tissue micro array, Whole mount, Internet), scanning machines, timestamps, H E staining protocols, and institutions. All histology images were captured using the bright field microscopy. For case use, these models were applied for the annotation tasks in clinician-oriented (cMDX) reports for prostatectomy specimens. The true positive rate (TPR) for slides with prostate cancer was 99.1 Gleason patterns reported in cMDX reports range between 0.795 and 1.0 at case level (n=55). TPR was 93.6 ductal morphology. The R-squared for the relative tumor volume was 0.987 between the ground truth and the prediction. Our models cover the major prostate pathology and successfully accomplish the annotation tasks.

READ FULL TEXT

Deep Learning for Prostate Pathology

A Multiresolution Deep Learning Framework for Automated Annotation of Reflectance Confocal Microscopy Images

Methods for Segmentation and Classification of Digital Microscopy Tissue Images

Hierarchical Deep Learning Classification of Unstructured Pathology Reports to Automate ICD-O Morphology Grading

Hierarchical Deep Learning Ensemble to Automate the Classification of Breast Cancer Pathology Reports by ICD-O Topography

Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard

Eliminating artefacts in Polarimetric Images using Deep Learning

Deep Learning for Prostate Pathology

Related Research

A Multiresolution Deep Learning Framework for Automated Annotation of Reflectance Confocal Microscopy Images

Methods for Segmentation and Classification of Digital Microscopy Tissue Images

Hierarchical Deep Learning Classification of Unstructured Pathology Reports to Automate ICD-O Morphology Grading

Hierarchical Deep Learning Ensemble to Automate the Classification of Breast Cancer Pathology Reports by ICD-O Topography

Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard

Eliminating artefacts in Polarimetric Images using Deep Learning