Multi-task Deep Learning for Colon Cancer Grading

Authors:

1 January 2020

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

p. 1-2
DOI:10.1109/iceic49074.2020.9051305

Abstract

Automated cancer grading is an important subject of study in digital pathology. In this paper, we introduce a multi-task learning approach to analyze digitized pathology images. The approach performs both classification and regression tasks in combination with a deep convolutional neural network to predict the tumor grade. Employing tissue microarrays (TMAs) and whole slide images (WSI), the proposed method achieved an accuracy of 85.91% in classifying colon tissues into four distinctive pathology classes, including benign and well differentiated, moderately differentiated, and poorly differentiated tumors.

Keywords

This publication has 4 references indexed in Scilit:

Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks
2019
Densely Connected Convolutional Networks
2017
Histopathological Image Analysis: A Review
IEEE Reviews in Biomedical Engineering, 2009
Multitask Learning
Machine Learning, 1997