Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Computer applications have considerably shifted from single data processing to machine
learning in recent years due to the accessibility and availability of massive volumes of data …

Medical image segmentation has been so far achieving promising results with Convolutional
Neural Networks (CNNs). However, it is arguable that in traditional CNNs, its pooling layer …

The task of multimodal cancer detection is to determine the locations and categories of
lesions by using different imaging techniques, which is one of the key research methods for …

When performing remote sensing image segmentation, practitioners often encounter various
challenges, such as a strong imbalance in the foreground–background, the presence of tiny …

Hepatocellular Carcinoma (HCC) detection, size grading, and quantification (ie the center
point coordinates, max-diameter, and area) by using multi-modality magnetic resonance …

Capsule network is a recent new deep network architecture that has been applied
successfully for medical image segmentation tasks. This work extends capsule networks for …

The timing of cell divisions in early embryos during the In-Vitro Fertilization (IVF) process is a
key predictor of embryo viability. However, observing cell divisions in Time-Lapse …

The brain is regarded as one of the most effective body-controlling organs. The development
of technology has enabled the early and accurate detection of brain tumors, which makes a …

Semantic segmentation of remote sensing images plays a vital role in land resource
management, yield estimation, and economic evaluation. Therefore, this paper proposes a …