Segmentation, Classification and Interpretation of Breast Cancer Medical Images using Human-in-the-Loop Machine Learning

arXiv:2403.20112v1 Announce Type: cross
Abstract: This paper explores the application of Human-in-the-Loop (HITL) strategies in training machine learning models in the medical domain. In this case a doctor-in-the-loop approach is proposed to leverage human expertise in dealing with large and complex data. Specifically, the paper deals with the integration of genomic data and Whole Slide Imaging (WSI) analysis of breast cancer. Three different tasks were developed: segmentation of histopathological images, classification of this images regarding the genomic subtype of the …

abstract application arxiv cancer case classification cs.cv cs.lg data deals doctor domain expertise hitl human images interpretation loop machine machine learning machine learning models medical paper segmentation strategies training type