A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the
potential for many fields—including medicine—to benefit from the insights that AI techniques …

Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …

Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …

Image instance segmentation involves labeling pixels of images with classes and instances,
which is one of the pivotal technologies in many domains, such as natural scenes …

Image segmentation is a fundamental problem in biomedical image analysis. Recent
advances in deep learning have achieved promising results on many biomedical image …

Colorectal cancer is one of the most common cancers worldwide, accounting for an annual
estimated 1.8 million incident cases. With the increasing number of colonoscopies being …

The analysis of glandular morphology within colon histopathology images is an important
step in determining the grade of colon cancer. Despite the importance of this task, manual …

Deep learning (DL) has achieved state-of-the-art performance in many digital pathology
analysis tasks. Traditional methods usually require hand-crafted domain-specific features …

Object segmentation and structure localization are important steps in automated image
analysis pipelines for microscopy images. We present a convolution neural network (CNN) …

Accurate segmentation of lung cancer in pathology slides is a critical step in improving
patient care. We proposed the ACDC@ LungHP (Automatic Cancer Detection and …