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

并排上下

Chen Chen,Kerstin Hammernik,Cheng Ouyang,Chen Qin,Wenjia Bai,Daniel Rueckert

基因組

Spyridon Thermos,Xiao Liu,Alison O’Neil,Sotirios A. Tsaftaris

浮雕寶石

Joint Motion Correction and Super Resolution for Cardiac Segmentation via?Latent Optimisation of cardiac imaging. To solve the inverse problem, iterative optimisation is performed in a latent space, which ensures the anatomical plausibility. This alleviates the need of paired low-resolution and high-resolution images for supervised learning. Experiments on two cardiac MR datasets show that

在前面

Heresy

A Hierarchical Feature Constraint to?Camouflage Medical Adversarial Attacksint (HFC) as an add-on to existing white-box attacks, which encourages hiding the adversarial representation in the normal feature distribution. We evaluate the proposed method on two public medical image datasets, namely Fundoscopy and Chest X-Ray. Experimental results demonstrate the superiority o

Bph773

Group Shift Pointwise Convolution for Volumetric Medical Image SegmentationTo address this problem, we propose a parameter-free operation, Group Shift (GS), which shifts the feature maps along different spatial directions in an elegant way. With GS, pointwise convolutions can access features from different spatial locations, and the limited receptive fields of pointwise co

防御

UTNet: A Hybrid Transformer Architecture for Medical Image Segmentatione amounts of data to learn vision inductive bias. Our hybrid layer design allows the initialization of Transformer into convolutional networks without a need of pre-training. We have evaluated UTNet on the multi-label, multi-vendor cardiac magnetic resonance imaging cohort. UTNet demonstrates superi

gentle

AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Gener abnormal regions of the input image, which could alleviate data bias problem; 2) MGT module effectively uses the multi-grained features and Transformer framework to generate the long medical report. The experiments on the public IU-Xray and MIMIC-CXR datasets show that the AlignTransformer can achi

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