Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021; 24th International C Marleen de Bruijne,Philippe C. Cattin,Caroli

Nuance

Focusing on Clinically Interpretable Features: Selective Attention Regularization for Liver Biopsy Ir an effective tool for image analysis. However, when applying deep learning methods to smaller histological image datasets, the model may be distracted by dominant normal tissues and ignore critical tissue alterations that pathologists focus on. In this paper, we propose a selective attention regul

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Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classificationdata. Studies showed that knowledge distillation (KD), especially the mean-teacher framework which is more robust to perturbations, can help mitigate the over-fitting effect. However, directly transferring KD from computer vision to medical image classification yields inferior performance as medical

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Tensor-Based Multi-index Representation Learning for Major Depression Disorder Detection with RestinfMRI) provides a non-invasive solution for the study of functional brain network abnormalities in MDD patients. Existing studies have shown that multiple indexes derived from rs-fMRI, such as fractional amplitude of low-frequency fluctuations (fALFF), voxel-mirrored homotopic connectivity (VMHC), an

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Region Ensemble Network for MCI Conversion Prediction with a Relation Regularized Lossormal areas are subtle compared to the size of the whole brain, 2) the features’ dimension is much larger than the number of samples. To tackle these problems, we propose a region ensemble model using a divide and conquer strategy to capture the disease’s finer representation. Specifically, the feat