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Pan-cancer computational histopathology reveals tumor mutational burden status through weakly-supervised deep learning
In comparison with the state-of-the-art TMB prediction model from previous publications, our multiscale model achieved better performance over previously reported models.
Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation
In this paper, we aim to boost the performance of semi-supervised learning for medical image segmentation with limited labels using a self-ensembling contrastive learning technique.
MMV-Net: A Multiple Measurement Vector Network for Multi-frequency Electrical Impedance Tomography
Deep learning has been extensively applied in solving the EIT inverse problem in biomedical and industrial process imaging.
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