Computational model discovery with reinforcement learning. - Maxime Bassenne; Adrián Lozano-Durán (CoRR, 2020)

Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images. - Hyunseok Seo; Charles W. Huang; Maxime Bassenne; Ruoxiu Xiao; Lei Xing 001 (IEEE Trans. Medical Imaging, 2020)

Time-Accurate and highly-Stable Explicit operators for stiff differential equations. - Maxime Bassenne; Lin Fu; Ali Mani (J. Comput. Phys., 2021)

Closing the Gap Between Deep Neural Network Modeling and Biomedical Decision-Making Metrics in Segmentation via Adaptive Loss Functions. - Hyunseok Seo; Maxime Bassenne; Lei Xing 001 (IEEE Trans. Medical Imaging, 2021)

Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation therapy. - Xiaokun Liang; Maxime Bassenne; Dimitre H. Hristov; Md Tauhidul Islam; Wei Zhao 029; Mengyu Jia; Zhicheng Zhang; Michael Gensheimer 001; Beth M. Beadle; Quynh Le; Lei Xing 001 (Comput. Biol. Medicine, 2022)

Image classification using graph neural network and multiscale wavelet superpixels. - Varun Vasudevan; Maxime Bassenne; Md Tauhidul Islam; Lei Xing 001 (Pattern Recognit. Lett., 2023)