Substantial progress has been made towards implementing automated radiology reporting
models based on deep learning (DL). This is due to the introduction of large medical …

Medical image segmentation involves identifying regions of interest in medical images. In
modern times, there is a great need to develop robust computer vision algorithms to perform …

The progress of some AI paradigms such as deep learning is said to be linked to an
exponential growth in the number of parameters. There are many studies corroborating …

Semantic segmentation requires both rich spatial information and sizeable receptive field.
However, modern approaches usually compromise spatial resolution to achieve real-time …

Learning to reliably perceive and understand the scene is an integral enabler for robots to
operate in the real-world. This problem is inherently challenging due to the multitude of …

Cervical cancer ranks the fourth most common cancer among females worldwide with
roughly 528, 000 new cases yearly. Around 85% of the new cases occurred in less …

SSD is a classical single-stage object detection algorithm, which predicts by generating
different scales of feature maps on different convolutional layers. However, due to the …

Weather Recognition plays an important role in our daily lives and many computer vision
applications. However, recognizing the weather conditions from a single image remains …

Breast cancer is a fatal disease and is a leading cause of death in women worldwide. The
process of diagnosis based on biopsy tissue is nontrivial, time‐consuming, and prone to …

To address the issues of high human interference and low efficiency in traditional manual
methods for classifying and rating steel scrap, we propose the development of CSBFNet, a …