Abstract Semi-Supervised Graph Clustering (SSGC) has emerged as a pivotal field at the
intersection of graph clustering and semi-supervised learning (SSL), offering innovative …

Background and objective Medicine image classification are important methods of traditional
medical image analysis, but the trainable data in medical image classification is highly …

Skin cancer is one of the common types of cancer. It spreads quickly and is not easy to
detect in the early stages, posing a major threat to human health. In recent years, deep …

Unsupervised domain adaptation (UDA) has been gradually applied in fundus image
segmentation, mitigating the challenge of insufficient data annotation by transferring pre …

Semi-supervised learning (SSL) has emerged as a powerful technique to mitigate the
scarcity of labeled data. However, the effectiveness of most SSL methods relies on the …

Worldwide, skin cancer prevalence necessitates accurate diagnosis to alleviate public
health burdens. Although the application of artificial intelligence in image analysis and …

In medical image analysis, semi-supervised learning (SSL) classification algorithms are
crucial due to the time-consuming and expensive nature of acquiring annotated medical …

为解决监督学习皮肤癌诊断模型的训练需要大量数据标注, 且医学专家标注工作成本高, 耗时长,
易疲劳等问题, 提出了一种基于自反馈阈值学习(Self-Feedback Threshold Learning, SFTL) …

This study introduces SkinLiTE, a lightweight supervised contrastive learning model, tailored
to enhance the detection and typification of skin lesions in dermoscopic images. The core of …

To address the challenges associated with the need for a large amount of annotated data in
supervised skin cancer diagnosis models, such as the high cost, time consumption, and …