Histopathological identification of tumor tissue is one of the routine pathological diagnoses
for pathologists. Recently, computational pathology has been successfully interpreted by a …

To detect abnormality from histopathology images in a patch-based convolutional neural
network (CNN), spatial context is an important cue. However, whole-slide image (WSI) is …

Objective This study aimed to develop an artificial intelligence model for predicting the
pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) of locally …

As common cancer, breast cancer kills thousands of women every year. It's significant to
provide doctors computer-aided diagnosis (CAD) to ease their workload as well as improve …

Deep neural networks have been applied in many computer vision tasks and achieved state-
of-the-art performance. However, misclassification will occur when DNN predicts adversarial …

Texture analysis of magnetic resonance T1 mapping with dilat... : Medicine Texture analysis of
magnetic resonance T1 mapping with dilated cardiomyopathy: A machine learning approach …

Worldwide breast cancer is one of the most frequent and mortal diseases across women.
Early, accurate metastasis cancer detection is a significant factor in raising the survival rate …

Advanced image analysis can lead to automated examination to histopatholgy images
which is essential for ob-jective and fast cancer diagnosis. Recently deep learning methods …

Adversarial training can improve the robustness of neural networks. Previous methods focus
on a single adversarial training strategy and do not consider the model property trained by …

Ultrasound-guided regional anesthesia (UGRA) can replace general anesthesia (GA),
improving pain control and recovery time. This method can be applied on the brachial …