Medical image segmentation plays a crucial role in various healthcare applications,
enabling accurate diagnosis, treatment planning, and disease monitoring. Traditionally …

The retinal image is a trusted modality in biomedical image-based diagnosis of many
ophthalmologic and cardiovascular diseases. Periodic examination of the retina can help in …

In recent years, there is been a growing reliance on image analysis methods to bolster
dentistry practices, such as image classification, segmentation and object detection …

Breast cancer poses a significant risk to women's health, and it is essential to provide proper
diagnostic support. Medical image processing technology is a key component of all …

Adversarial attacks pose a significant threat to deep learning models, specifically medical
images, as they can mislead models into making inaccurate predictions by introducing …

In recent years, deep learning technology for clinical diagnosis has progressed
considerably, and the value of medical imaging continues to increase. In the past, clinicians …

Polycystic ovary syndrome (PCOS) and endometrial carcinoma (EC) are gynecological
conditions that have attracted significant attention due to the higher prevalence of EC in …

For the problems of blurred edges, uneven background distribution, and many noise
interferences in medical image segmentation, we proposed a medical image segmentation …

The precise and automatic recognition of retinal vessels is of utmost importance in the
prevention, diagnosis and assessment of certain eye diseases, yet it brings a nontrivial …

When performing classification tasks on high-dimensional data, traditional machine learning
algorithms often fail to filter out valid information in the features adequately, leading to low …