Strategy
发表于 2025-3-21 17:26:30
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甜得发腻
发表于 2025-3-21 22:42:08
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entitle
发表于 2025-3-22 03:59:43
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
芭蕾舞女演员
发表于 2025-3-22 07:01:36
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苦笑
发表于 2025-3-22 10:52:37
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
发表于 2025-3-22 15:13:22
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树木中
发表于 2025-3-22 18:12:59
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
闲逛
发表于 2025-3-22 21:44:03
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枪支
发表于 2025-3-23 05:18:47
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
发表于 2025-3-23 05:43:50
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