Multi-task Deep Learning for Colon Cancer Grading
Trinh Thi Le Vuong, Daigeun Lee, Jin Tae Kwak, Kyungeun Kim
20202020 International Conference on Electronics, Information, and Communication (ICEIC)24 citationsDOI
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.
Topics & Concepts
Grading (engineering)Computer scienceArtificial intelligenceConvolutional neural networkDeep learningDigital pathologyTissue microarrayPattern recognition (psychology)Task (project management)Colorectal cancerPathologyCancerMedicineImmunohistochemistryBiologyInternal medicineManagementEcologyEconomicsAI in cancer detectionRadiomics and Machine Learning in Medical ImagingMedical Imaging and Analysis