TY - GEN
T1 - Generating Counterfactual Explanations For Causal Inference in Breast Cancer Treatment Response
AU - Zhou, Siqiong
AU - Pfeiffer, Nicholaus
AU - Islam, Upala J.
AU - Banerjee, Imon
AU - Patel, Bhavika K.
AU - Iquebal, Ashif S.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - counterfactual explanations
KW - machine learning
KW - magnetic resonance imaging
KW - radiomics
UR - http://www.scopus.com/inward/record.url?scp=85141690062&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85141690062&partnerID=8YFLogxK
U2 - 10.1109/CASE49997.2022.9926519
DO - 10.1109/CASE49997.2022.9926519
M3 - Conference contribution
AN - SCOPUS:85141690062
T3 - IEEE International Conference on Automation Science and Engineering
SP - 955
EP - 960
BT - 2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022
PB - IEEE Computer Society
T2 - 18th IEEE International Conference on Automation Science and Engineering, CASE 2022
Y2 - 20 August 2022 through 24 August 2022
ER -