Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …

Aims A large number of clinical trials have proved that if breast cancer is diagnosed at an
early stage, it could give patients more treatment options and improve the treatment effect …

The lack of representative features between benign nodules, especially level 3 of Thyroid
Imaging Reporting and Data System (TI-RADS), and malignant nodules limits diagnostic …

The need for time and attention, given by the doctor to the patient, due to the increased
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …

Semi-supervised learning has attracted much attention in medical image analysis as
annotating medical images is substantially difficult. Existing semi-supervised learning …

Breast cancer is the second most dominant kind of cancer among women. Breast Ultrasound
images (BUI) are commonly employed for the detection and classification of abnormalities …

As a primary computation unit, a processing element (PE) is key to the energy efficiency of a
convolutional neural network (CNN) accelerator. Taking advantage of the inherent error …

In recent years, various data-driven algorithms have been applied to the classification and
staging of brain glioma MRI detection. However, the restricted availability of brain glioma …

The performance of deep learning models generally relies on large and high-quality labeled
datasets. However, in medical domain, as labeling process is much more laborious and time …

Convolutional neural networks (CNNs) are currently the best computational methods for the
diagnosis of Alzheimer's disease (AD) from neuroimaging. CNNs are able to automati-cally …