MF-OMKT: Model fusion based on online mutual knowledge transfer for breast cancer histopathological image classification.

Pathological diagnosis is considered as the benchmark for the detection of breast cancer. With the increasing number of patients, computer-aided histopathological image classification can assist pathologists in improving breast cancer diagnosis accuracy and working efficiency. However, a single model is insufficient for effective diagnosis, and this also does not accord with the principle of centralized decision-making.

TiM-Net: Transformer in M-Net for Retinal Vessel Segmentation.

retinal image is a crucial window for the clinical observation of cardiovascular, cerebrovascular, or other correlated diseases. Retinal vessel segmentation is of great benefit to the clinical diagnosis. Recently, the convolutional neural network (CNN) has become a dominant method in the retinal vessel segmentation field, especially the U-shaped CNN models.

DCML: Deep contrastive mutual learning for COVID-19 recognition.

COVID-19 is a form of disease triggered by a new strain of coronavirus. Automatic COVID-19 recognition using computer-aided methods is beneficial for speeding up diagnosis efficiency. Current researches usually focus on a deeper or wider neural network for COVID-19 recognition.

[Effect of tongbian navel paste on colonic motility in children with constipation of slow transmission type].

Objective: To observe the therapeutic effect of Tongbian Navel Paste (TBP, a self-formulated preparation consisting of both Chinese herbal and Western medicines) on children with constipation of slow transmission type (CSTT) and it influence on patients' colonic motility.

Methods: Sixty-eight children with CSTT were randomly assigned to two groups, 38 in the treatment group treated with TBP, and 30 in the control group treated with oral taking Maren Zipi pill. The changes of clinical symptoms, and the outcomes of colon transmission test were observed and compared before and after treatment.