DRIMV_TSK: An Interpretable Surgical Evaluation Model for Incomplete Multi-View Rectal Cancer Data

TL;DR

This paper introduces DRIMV_TSK, an interpretable AI model for evaluating rectal cancer surgery difficulty using incomplete multi-view data, combining dual representation learning, view imputation, and fuzzy systems for improved accuracy.

Contribution

The paper presents a novel dual representation incomplete multi-view learning model with integrated view imputation and a TSK fuzzy system for surgical evaluation.

Findings

DRIMV_TSK outperforms several advanced algorithms on the MVRC dataset.

The model effectively handles incomplete multi-view data with high accuracy.

Incorporating view weights via Shannon entropy improves evaluation robustness.

Abstract

A reliable evaluation of surgical difficulty can improve the success of the treatment for rectal cancer and the current evaluation method is based on clinical data. However, more data about rectal cancer can be collected with the development of technology. Meanwhile, with the development of artificial intelligence, its application in rectal cancer treatment is becoming possible. In this paper, a multi-view rectal cancer dataset is first constructed to give a more comprehensive view of patients, including the high-resolution MRI image view, pressed-fat MRI image view, and clinical data view. Then, an interpretable incomplete multi-view surgical evaluation model is proposed, considering that it is hard to obtain extensive and complete patient data in real application scenarios. Specifically, a dual representation incomplete multi-view learning model is first proposed to extract the common information between views and specific information in each view. In this model, the missing view imputation is integrated into representation learning, and second-order similarity constraint is also introduced to improve the cooperative learning between these two parts. Then, based on the imputed multi-view data and the learned dual representation, a multi-view surgical evaluation model with the TSK fuzzy system is proposed. In the proposed model, a cooperative learning mechanism is constructed to explore the consistent information between views, and Shannon entropy is also introduced to adapt the view weight. On the MVRC dataset, we compared it with several advanced algorithms and DRIMV_TSK obtained the best results.