2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-MIA.9
Paper Title EXPLAINABLE PREDICTION OF RENAL CELL CARCINOMA FROM CONTRAST-ENHANCED CT IMAGES USING DEEP CONVOLUTIONAL TRANSFER LEARNING AND THE SHAPLEY ADDITIVE EXPLANATIONS APPROACH
Authors Fuchang Han, Shenghui Liao, Siming Yuan, Renzhong Wu, Yuqian Zhao, Yu Xie, Central South University, China
SessionSS-MIA: Special Session: Deep Learning and Precision Quantitative Imaging for Medical Image Analysis
LocationArea A
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Special Sessions: Deep Learning and Precision Quantitative Imaging for Medical Image Analysis
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Abstract The prediction of renal cell carcinoma (RCC) is an important cancer screening step. Existing state-of-the-art methods focus on developing machine/deep learning networks with one or more optimization strategies for higher identification accuracies. Such developments ignore the interpretability and clinical utility of models, which are still quite opaque to clinicians. This paper introduces deep convolutional transfer learning and SHapley Additive exPlanations (SHAP) to the classification model and proposes an explainable RCC prediction model. The model evaluates the risks and benefits using decision curve analysis (DCA). Specifically, multiscale feature extraction and compensation are proposed to enrich the representations. By combining the high-importance features in a parallel manner, the models’ performances are gradually enhanced. Our model achieves an accuracy of 73.87% and an area under the curve (AUC) of 0.8030 on the Hunan Cancer Hospital dataset. To demonstrate the generalizability, our model yields an accuracy of 99.81% on the public COIL-100 dataset.