Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-
169 model. However, the current system for identifying metastases in a lymph node is …

Breast cancer patients often have recurrence and metastasis after surgery. Predicting the
risk of recurrence and metastasis for a breast cancer patient is essential for the development …

Background: Metastasis is the main cause of death toll among breast cancer patients. Since
current approaches for diagnosis of lymph node metastases are time-consuming, deep …

The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in
women. Early diagnosis has improved further treatments and reduced the level of mortality …

Background Deep learning (DL) algorithms are gaining extensive attention for their excellent
performance in image recognition tasks. DL models can automatically make a quantitative …

Deep learning (DL) algorithms have been proven to be very effective in a wide range of
computer vision applications, such as segmentation, classification, and detection. DL …

Abstract Background Approximately 10%–15% of patients with breast cancer die of cancer
metastasis or recurrence, and early diagnosis of it can improve prognosis. Breast cancer …

Background: It is important to be able to predict, for each individual patient, the likelihood of
later metastatic occurrence, because the prediction can guide treatment plans tailored to a …

Predicting TNM stage is the major determinant of breast cancer prognosis and treatment.
The essential part of TNM stage classification is whether the cancer has metastasized to the …

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