Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …

Medical imaging plays a significant role in different clinical applications such as medical
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …

Despite the development of non-invasive methods, bone histomorphometry remains the only
method to analyze bone at the tissue and cell levels. Quantitative analysis of transiliac bone …

High throughput nuclear segmentation and classification of whole slide images (WSIs) is
crucial to biological analysis, clinical diagnosis and precision medicine. With the advances …

Digital histology images are amenable to the application of convolutional neural networks
(CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally …

Background and objective Cervical cell classification has important clinical significance in
cervical cancer screening at early stages. In contrast with the conventional classification …

Histopathological image analysis is the gold standard for pathologists to grade colorectal
cancers of different differentiation types. However, the diagnosis by pathologists is highly …

Cytology is the first pathological examination performed in the diagnosis of lung cancer. In
our previous study, we introduced a deep convolutional neural network (DCNN) to …

CAD systems for histopathology image analysis using machine learning is a well
researched subject. Deep learning is playing a major role in advancing this research in the …

Automatic tissue classification is a fundamental task in computational pathology for profiling
tumor micro-environments. Deep learning has advanced tissue classification performance at …