Digital whole slides images contain an enormous amount of information providing a strong
motivation for the development of automated image analysis tools. Particularly deep neural …

The vision transformer (ViT) is a state-of-the-art architecture for image recognition tasks that
plays an important role in digital health applications. Medical images account for 90% of the …

Using deep learning models to diagnose cancer from histology data presents several
challenges. Cancer grading and localization of regions of interest (ROIs) in these images …

Transformers have been widely used in many computer vision challenges and have shown
the capability of producing better results than convolutional neural networks (CNNs). Taking …

Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …

Molecular and genomic properties are critical in selecting cancer treatments to target
individual tumors, particularly for immunotherapy. However, the methods to assess such …

Abstract Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disorder
characterized by motor neuron degeneration. Significant research has begun to establish …

Abstract Vision Transformers (ViTs) have become ubiquitous in computer vision. Despite
their success, ViTs lack inductive biases, which can make it difficult to train them with limited …

Digital pathology technologies, including whole slide imaging (WSI), have significantly
improved modern clinical practices by facilitating storing, viewing, processing, and sharing …

Multi-instance learning, a commonly used technique in artificial intelligence for analyzing
slides, can be applied to diagnose thyroid cancer based on cytological smears. Since …