CNN Based survivability prediction Using Pathological Image of Soft Tissue Tumor
Yasuhide Nonaka, Kento Morita, Tomohito Hagi, Tomoki Nakamura, Kunihiro Asanuma, Akihiro Sudo, Katsunori Uchida, Tetsushi Wakabayashi · 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2022
The number of patients of malignant soft tissue tumor is about 3,000 every year in Japan. In the treatment procedure of soft tissue tumor, survivability of patient must be predicted based on experience. The miss-prediction causes the unnecessary treatment or patient death, an objective decision making system based on pathological image is required. This paper proposes a convolutional neural network (CNN) based survivability and survival time prediction method using pathological image of soft tissue tumor. The proposed method trained Inception v3 and ResNetl4-based modified CNN model using 47 pathological images of 26 subjects. As the result of 4-fold cross validation test, the image-wise survivability, image-wise non-survivability, subject-wise survivability, and subject-wise non-survivability were predicted in F-measure of 0.847, 0.743, 0.909 and 0.824, respectively. Additional experiment using non-survival patients showed that the survival time was predicted in 4.57 months error.