Automated detection of cancer metastases in lymph nodes has the potential to improve the
assessment of prognosis for patients. To enable fair comparison between the algorithms for …

The pathological identification of lymph node (LN) metastasis is demanding and tedious.
Although convolutional neural networks (CNNs) possess considerable potential in …

Background The presence of lymph node metastases is one of the most important factors in
breast cancer prognosis. The most common way to assess regional lymph node status is the …

Importance Application of deep learning algorithms to whole-slide pathology images can
potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of …

The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to
evaluate computational systems for the automated detection of metastatic breast cancer in …

Screening of lymph node metastases in colorectal cancer (CRC) can be a cumbersome task,
but it is amenable to artificial intelligence (AI)-assisted diagnostic solution. Here, we propose …

Simple Summary The assistance of computer image analysis that automatically identifies
tissue or cell types has greatly improved histopathologic interpretation and diagnosis …

Context.—Nodal metastasis of a primary tumor influences therapy decisions for a variety of
cancers. Histologic identification of tumor cells in lymph nodes can be laborious and error …

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 …

Metastasis detection of lymph nodes in Whole-slide Images (WSIs) plays a critical role in the
diagnosis of breast cancer. Automatic metastasis detection is a challenging issue due to the …