Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …

Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to
overcome challenges of data silos and data sensibility. Exactly what research is carrying the …

Digital pathology has transformed the traditional pathology practice of analyzing tissue
under a microscope into a computer vision workflow. Whole slide imaging allows …

Although deep learning-based computer-aided diagnosis systems have recently achieved
expert-level performance, develo** a robust model requires large, high-quality data with …

This paper proposes a novel method for sports video scene classification with the particular
intention of video summarization. Creating and publishing a shorter version of the video is …

This review highlights the efficacy of combining federated learning (FL) and transfer learning
(TL) for cancer detection via image analysis. By integrating these techniques, research has …

The accuracy and robustness of image classification with supervised deep learning are
dependent on the availability of large-scale labelled training data. In medical imaging, these …

Breast cancer is a common cancer among women, and screening mammography is the
primary tool for diagnosing this condition. Recent advancements in deep-learning …

Diabetic retinal image classification aims to conduct diabetic retinopathy automatically
diagnosing, which has achieved considerable improvement by deep learning models …

In industrial production, flaws and defects inevitably appear on surfaces, resulting in
unqualified products. Therefore, surface defect detection plays a key role in ensuring …