Breast cancer diagnosis is inherently multimodal. To assess a patient's cancer status,
physicians integrate imaging findings with a variety of clinical risk factor data. Despite this …

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and ultrasound (US),
which are two common modalities for clinical breast tumor diagnosis besides Mammograms …

Background Deep learning algorithms significantly improve the accuracy of pathological
image classification, but the accuracy of breast cancer classification using only single-mode …

Magnetic resonance imaging (MRI) is the recommended imaging modality in the diagnosis
of breast cancer. However, each MRI scan comprises dozens of volumes for the radiologist …

Breast cancer has the highest incidence and second highest mortality rate for women in the
US. Our study aims to utilize deep learning for benign/malignant classification of …

Digital image-based signatures of breast tumors may ultimately contribute to the design of
patient-specific breast cancer diagnostics and treatments. Beyond traditional human …

Automatic breast image classification plays an important role in breast cancer diagnosis,
and multi-modality image fusion may improve classification performance. However, existing …

Diagnostic interpretation of breast MRI studies requires meticulous work and a high level of
expertise. Computerized algorithms can assist radiologists by automatically characterizing …

Background The classification of benign versus malignant breast lesions on multi-sequence
Magnetic Resonance Imaging (MRI) is a challenging task since breast lesions are …

One issue with computer based histopathology image analysis is that the size of the raw
image is usually very large. Taking the raw image as input to the deep learning model would …