Breast cancer is the second leading cause of death for women, so accurate early detection
can help decrease breast cancer mortality rates. Computer-aided detection allows …

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

Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient
and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients …

In this paper, we aim to provide a survey on the applications of deep learning for cancer
detection and diagnosis and hope to provide an overview of the progress in this field. In the …

This review covers computer-assisted analysis of images in the field of medical imaging.
Recent advances in machine learning, especially with regard to deep learning, are helping …

Automated breast cancer multi-classification from histopathological images plays a key role
in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is …

We present recursive cascaded networks, a general architecture that enables learning deep
cascades, for deformable image registration. The proposed architecture is simple in design …

Stereo matching algorithms usually consist of four steps, including matching cost calculation,
matching cost aggregation, disparity calculation, and disparity refinement. Existing CNN …

Glaucoma is one of the leading causes of irreversible vision loss. Many approaches have
recently been proposed for automatic glaucoma detection based on fundus images …

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer
patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and …