The Relationship Between Alzheimer's Disease and Pyroptosis and the Intervention Progress of Traditional Chinese Medicine.

Alzheimer's disease (AD) is a progressive neurodegenerative disease caused by multiple causes. The main pathological features of AD are β-amyloid (Aβ) deposition, hyperphosphorylation of Tau protein, and progressive neuronal loss. Pyroptosis is one of the main forms of neuronal death, which is mainly caused by the activation of Gasdermin protein by upstream signals and the release of its N-terminal domain on the cell membrane.

Unsupervised Local Discrimination for Medical Images.

Contrastive learning, which aims to capture general representation from unlabeled images to initialize the medical analysis models, has been proven effective in alleviating the high demand for expensive annotations. Current methods mainly focus on instance-wise comparisons to learn the global discriminative features, however, pretermitting the local details to distinguish tiny anatomical structures, lesions, and tissues. To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images.

Pairwise learning for medical image segmentation.

Fully convolutional networks (FCNs) trained with abundant labeled data have been proven to be a powerful and efficient solution for medical image segmentation. However, FCNs often fail to achieve satisfactory results due to the lack of labelled data and significant variability of appearance in medical imaging. To address this challenging issue, this paper proposes a conjugate fully convolutional network (CFCN) where pairwise samples are input for capturing a rich context representation and guide each other with a fusion module.

Rectifying Supporting Regions With Mixed and Active Supervision for Rib Fracture Recognition.

Automatic rib fracture recognition from chest X-ray images is clinically important yet challenging due to weak saliency of fractures. Weakly Supervised Learning (WSL) models recognize fractures by learning from large-scale image-level labels. In WSL, Class Activation Maps (CAMs) are considered to provide spatial interpretations on classification decisions.

Weakly Supervised Lesion Detection From Fundus Images.

Early diagnosis and continuous monitoring of patients suffering from eye diseases have been major concerns in the computer-aided detection techniques. Detecting one or several specific types of retinal lesions has made a significant breakthrough in computer-aided screen in the past few decades. However, due to the variety of retinal lesions and complex normal anatomical structures, automatic detection of lesions with unknown and diverse types from a retina remains a challenging task.