Explanation Generation via Decompositional Rules Extraction for Head and Neck Cancer Classification
| Type of publication: | Inproceedings |
| Citation: | |
| Booktitle: | Explainable and Transparent AI and Multi-Agent Systems |
| Year: | 2023 |
| Month: | June |
| Abstract: | Human papillomavirus (HPV) accounts for 60% of head and neck (H&N) cancer cases. Assessing the tumor extension (tumor grading) and determining whether the tumor is caused by HPV infection (HPV status) is essential to select the appropriate treatment. Therefore, developing non-invasive, transparent (trustworthy), and reliable methods is imperative to tailor the treatment to patients based on their status. Some studies have tried to use radiomics features extracted from positron emission tomography (PET) and computed tomography (CT) images to predict HPV status. However, to the best of our knowledge, no research has been conducted to explain (e.g., via rule sets) the internal decision process executed on deep learning (DL) predictors applied to HPV status prediction and tumor grading tasks. This study employs a decompositional rule extractor (namely DEXiRE) to extract explanations in the form of rule sets from DL predictors applied to H&N cancer diagnosis. The extracted rules can facilitate researchers’ and clinicians’ understanding of the model’s decisions (making them more transparent) and can serve as a base to produce semantic and more human-understandable explanations. |
| Keywords: | Feature ranking, Global explainability, HPV status explanation. TNM explanation, Local explainability, rule extraction |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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