Accurate and Feasible Deep Learning Based Semi-Automatic Segmentation in CT for Radiomics Analysis in Pancreatic Neuroendocrine Neoplasms. - Bingsheng Huang; Xiaoyi Lin; Jingxian Shen; Xin Chen 025; Jia Chen; Zi-Ping Li; Mingyu Wang; Chenglang Yuan; Xian-Fen Diao; Yanji Luo; Shi-Ting Feng (IEEE J. Biomed. Health Informatics, 2021)

Deep Semantic Segmentation Feature-Based Radiomics for the Classification Tasks in Medical Image Analysis. - Bingsheng Huang; Junru Tian; Hongyuan Zhang 002; Zixin Luo; Jing Qin 001; Chen Huang; Xueping He; Yanji Luo; Yongjin Zhou 002; Guo Dan; Hanwei Chen; Shi-Ting Feng; et al. (IEEE J. Biomed. Health Informatics, 2021)

Integrating with Segmentation by Using Multi-Task Learning Improves Classification Performance in Medical Image Analysis. - Yi Li; Yuanyuan Zhao; Mingyu Wang; Fei Li; Jia Chen; Yanji Luo; Shi-Ting Feng; Xiaoyi Lin; Bingsheng Huang (CBMS, 2022)

Identifying patients with Crohn's disease at high risk of primary nonresponse to infliximab using a radiomic-clinical model. - Xuehua Li; Yingkui Zhong; Chenglang Yuan; Jinjiang Lin; Xiaodi Shen; Minyi Guo; Baolan Lu; Jixin Meng; Yangdi Wang; Naiwen Zhang; Zixin Luo; Guimeng Hu; et al. (Int. J. Intell. Syst., 2022)

Improving Tumor Classification by Reusing Self-Predicted Segmentation of Medical Images as Guiding Knowledge. - Xiaoyi Lin; Mingyu Wang; Fei Li; Ziyue Xu 001; Jia Chen; Xin Chen 025; Chenglang Yuan; Songxiong Wu; Yanji Luo; Jingxian Shen; Shi-Ting Feng; Bingsheng Huang (IEEE J. Biomed. Health Informatics, 2024)