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

Vaginismus

NUDGE

背心

RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs Using a Novel Multi-scale Generatures from the discriminator’s decoder over the encoder. Doing so combined with the fact that the discriminator’s decoder attempts to determine real or fake images at the pixel level better preserves macro and microvascular structure. By combining reconstruction and weighted feature matching loss, t

peptic-ulcer

MIL-VT: Multiple Instance Learning Enhanced Vision Transformer for Fundus Image Classificationniently attached to the Vision Transformer in a plug-and-play manner and effectively enhances the model performance for the downstream fundus image classification tasks. The proposed MIL-VT framework achieves superior performance over CNN models on two publicly available datasets when being trained

你正派

cunning

BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifyinentation performance, resulting in more accurate FAZ contours and fewer outliers. Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diag

万灵丹

ANIM

陶器

Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resol-category classification tasks that are leaned by two newly designed high-resolution feature extractors (HRFEs). The two HRFEs share the same backbone encoder with W-Net by a composite connection so that meaningful features for the segmentation task can be inherited to the classification task. Last,

编辑才信任

Prototypical Models for Classifying High-Risk Atypical Breast Lesionsing clinically relevant explanations to its recommendations, thus it is intrinsically explainable, which is a major contribution of this work. Our experiments also show state-of-the-art performance in recall compared to the latest deep-learning based graph neural networks (GNNs).