Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay

Strategy

書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023影響因子(影響力)




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023影響因子(影響力)學科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023網絡公開度




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023網絡公開度學科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023被引頻次




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023被引頻次學科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023年度引用




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023年度引用學科排名




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023讀者反饋




書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2023讀者反饋學科排名

甜得發(fā)膩

entitle

Towards Novel Class Discovery: A Study in?Novel Skin Lesions Clustering recognize samples from predefined categories, when they are deployed in the clinic, data from new unknown categories are constantly emerging. Therefore, it is crucial to automatically discover and identify new semantic categories from new data. In this paper, we propose a new novel class discovery

芭蕾舞女演員

苦笑

A Style Transfer-Based Augmentation Framework for?Improving Segmentation and?Classification Performassification performance of deep learning models for ultrasound image analysis. Previous studies have attempted to solve this problem by using style transfer and augmentation techniques, but these methods usually require a large amount of data from multiple sources and source-specific discriminators,

SPECT

樹木中

SegNetr: Rethinking the Local-Global Interactions and Skip Connections in U-Shaped Networks-shaped segmentation networks: 1) mostly focus on designing complex self-attention modules to compensate for the lack of long-term dependence based on convolution operation, which increases the overall number of parameters and computational complexity of the network; 2) simply fuse the features of e

閑逛

槍支

Multi-modality Contrastive Learning for?Sarcopenia Screening from?Hip X-rays and?Clinical Informatiormal muscle strength. Accurate screening for sarcopenia is a key process of clinical diagnosis and therapy. In this work, we propose a novel multi-modality contrastive learning (MM-CL) based method that combines hip X-ray images and clinical parameters for sarcopenia screening. Our method captures t

正式通知

DiffMIC: Dual-Guidance Diffusion Network for?Medical Image Classificationer vision community. However, while a substantial amount of diffusion-based research has focused on generative tasks, few studies have applied diffusion models to general medical image classification. In this paper, we propose the first diffusion-based model (named DiffMIC) to address general medica