ArticleIEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering2022
Generating Counterfactual Explanations For Causal Inference in Breast Cancer Treatment Response.
Article in IEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A literature review of artificial intelligence (AI) for medical image segmentation: from AI and explainable AI to trustworthy AI.Quantitative imaging in medicine and surgery · 2024Review
- SCGAN: Sparse CounterGAN for Counterfactual Explanations in Breast Cancer Prediction.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2024Article
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Authors and funding
6 authors.
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Abstract
Imaging phenotypes extracted via radiomics of magnetic resonance imaging has shown great potential at predicting the treatment response in breast cancer patients after administering neoadjuvant systemic therapy (NST). Existing machine learning models are, however, limited in providing an expert-level interpretation of these models, particularly interpretability towards generating causal inference. Causal relationships between imaging phenotypes, clinical information, molecular features, and the treatment response may be useful in guiding the treatment strategies, management plans, and gaining acceptance in medical communities. In this work, we leverage the concept of counterfactual explanations to extract causal relationships between various imaging phenotypes, clinical information, molecular features, and the treatment response after NST. We implement the methodology on a publicly available breast cancer dataset and demonstrate the causal relationships generated from counterfactual explanations. We also compare and contrast our results with traditional explanations, such as LIME and Shapley.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.