Hierarchical visual case-based reasoning for supporting breast cancer therapy

Jean-Baptiste Lamy (1) , Boomadevi Sekar , Gilles Guezennec , Jacques Bouaud (1) , Brigitte Séroussi (1)

1 LIMICS - Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé

Breast cancer therapy is particularly complex. Case-based reasoning (CBR) is an approach that can support clinicians when prescribing a therapy, and that is able to explain its recommendation to the clinicians. In a previous work, we proposed a visual CBR approach for helping clinicians to choose a treatment between four main categories (e.g. surgery, chemotherapy). However, these are broad categories and clinicians need more details about the treatment, e.g. several surgeries exist such as lumpectomy. Here, we extend our visual CBR approach for fully supporting the therapy for breast cancer, using a hierarchical approach: first, decide the category, then decide the exact treatment, etc. Index Terms-breast cancer, case-based reasoning, explainable artificial intelligence, XAI.

Domaines: Santé publique et épidémiologie; Intelligence artificielle [cs.AI]

Jean-Baptiste Lamy, Boomadevi Sekar, Gilles Guezennec, Jacques Bouaud, Brigitte Séroussi. Hierarchical visual case-based reasoning for supporting breast cancer therapy. 2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME), Oct 2019, Tripoli, Lebanon. pp.1-4, ⟨10.1109/ICABME47164.2019.8940223⟩. ⟨hal-03479269⟩