Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020; 23rd International C Anne L. Martel,Purang Abolmaesumi,Leo Joskow

ALLY

书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020影响因子(影响力)




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020影响因子(影响力)学科排名




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020网络公开度




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020网络公开度学科排名




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020被引频次




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020被引频次学科排名




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020年度引用




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020年度引用学科排名




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020读者反馈




书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2020读者反馈学科排名

LAP

Scribble2Label: Scribble-Supervised Cell Segmentation via Self-generating Pseudo-Labels with Consistthroughput cell segmentation has become feasible. However, most existing deep learning-based cell segmentation algorithms require fully annotated ground-truth cell labels, which are time-consuming and labor-intensive to generate. In this paper, we introduce ., a novel weakly-supervised cell segmenta

osteopath

Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy ns to a trained pathology expert. However, to match human performance, the methods rely on the availability of vast amounts of high-quality labeled data, which poses a significant challenge. To circumvent this, augmented labeling methods, also known as expert-algorithm-collaboration, have recently b

马具

CRANK

皱痕

Synthetic Sample Selection via Reinforcement Learningystems. However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images. Thus, the effectiveness of those synthetic

赏心悦目

Dual-Level Selective Transfer Learning for Intrahepatic Cholangiocarcinoma Segmentation in Non-enhanlinical decision making. While deep neural networks offer an effective tool for ICC segmentation, collecting large amounts of annotated data for deep network training may not be practical for this kind of applications. To this end, transfer learning approaches utilize abundant data from similar task

excursion

BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecturelution, image denoising, and inpainting. Previous extensions of U-Net have focused mainly on the modification of its existing building blocks or the development of new functional modules for performance gains. As a result, these variants usually lead to an unneglectable increase in model complexity.

GOAD

Constrain Latent Space for Schizophrenia Classification via Dual Space Mapping Net only perform a simple binary classification with high-dimensional neuroimaging features that ignore individual’s unique clinical symptoms. And the biomarkers mined in this way are more susceptible to confounding factors such as demographic factors. To address these questions, we propose a novel end

手势

Have You Forgotten? A Method to Assess if Machine Learning Models Have Forgotten Datae several providers contribute data to a consortium for the joint development of a classification model (hereafter the target model), but, now one of the providers decides to leave. This provider requests that their data (hereafter the query dataset) be removed from the databases but also that the m