Segmentation, Classification and Interpretation of Breast Cancer Medical
  Images using Human-in-the-Loop Machine Learning
David Vázquez-Lema Eduardo Mosqueira-Rey Elena Hernández-Pereira Carlos Fernández-Lozano Fernando Seara-Romera Jorge Pombo-Otero
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 cancer and,
finally, interpretation of the machine learning results. The involvement of a
pathologist helped us to develop a better segmentation model and to enhance the
explainatory capabilities of the models, but the classification results were
suboptimal, highlighting the limitations of this approach: despite involving
human experts, complex domains can still pose challenges, and a HITL approach
may not always be effective.