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

The widespread adoption of whole slide imaging has increased the demand for effective
and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision …

Traditional image-based survival prediction models rely on discriminative patch labeling
which make those methods not scalable to extend to large datasets. Recent studies have …

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 …

The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis.
However, due to the large scale of WSIs and various sizes of the abnormal area, how to …

Digital histology images are amenable to the application of convolutional neural networks
(CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally …

Automated digital histopathology image segmentation is an important task to help
pathologists diagnose tumors and cancer subtypes. For pathological diagnosis of cancer …

Given the recent success of artificial intelligence (AI) in computer vision applications, many
pathologists anticipate that AI will be able to assist them in a variety of digital pathology …

Breast cancer diagnosis often requires accurate detection of metastasis in lymph nodes
through Whole-slide Images (WSIs). Recent advances in deep convolutional neural …

Lymph node metastasis is one of the most important indicators in breast cancer diagnosis,
that is traditionally observed under the microscope by pathologists. In recent years, with the …