Medical image segmentation plays a crucial role in various healthcare applications,
enabling accurate diagnosis, treatment planning, and disease monitoring. Traditionally …

Vision transformers have become popular as a possible substitute to convolutional neural
networks (CNNs) for a variety of computer vision applications. These transformers, with their …

Tumor-infiltrating lymphocytes, specialized immune cells, are considered an important
biomarker in cancer analysis. Automated lymphocyte detection is challenging due to its …

One of the common and promising deep learning approaches used for medical image
segmentation is transformers, as they can capture long-range dependencies among the …

The segmentation of cell nuclei in tissue images stained with the blood dye hematoxylin and
eosin (H $\& $ E) is essential for various clinical applications and analyses. Due to the …

Detection of Tumor-Infiltrating Lymphocytes (TILs) has a high prognostic value in cancer
diagnosis due to their ability to identify and kill cancer cells. However, this task is non-trivial …

Monocular depth estimation technology is widely utilized in autonomous driving for sensing
and obstacle avoidance. Recent advancements in deep-learning techniques have resulted …

Transformers, due to their ability to learn long range dependencies, have overcome the
shortcomings of convolutional neural networks (CNNs) for global perspective learning …

The digitization of different components of industry and inter-connectivity among indigenous
networks have increased the risk of network attacks. Designing an intrusion detection …

The deployment of advanced deep learning models for medical image segmentation is often
constrained by the requirement for extensively annotated datasets. Weakly-supervised …