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Hierarchical visual case-based reasoning for supporting breast cancer therapy

Authors: Lamy, Jean-Baptiste; Sekar, Boomadevi; Guezennec, Gilles; Bouaud, Jacques; Séroussi, Brigitte;

Hierarchical visual case-based reasoning for supporting breast cancer therapy

Abstract

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 (surgery, chemother- apy,...). 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.

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France
Keywords

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], breast cancer, explainable artificial intelligence, XAI, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, case-based reasoning

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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