Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have
led to significant progress in image processing. Since 2016, many applications for the …

Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation
phase in many fields, including medicine. The combination of improved availability of large …

Medical image classification plays an essential role in clinical treatment and teaching tasks.
However, the traditional method has reached its ceiling on performance. Moreover, by using …

We present DeepVesselNet, an architecture tailored to the challenges faced when extracting
vessel trees and networks and corresponding features in 3-D angiographic volumes using …

Data-driven approaches have great potential to shape future practices in radiology. The
most straightforward strategy to obtain clinically accurate models is to use large, well …

Abstract With Artificial Intelligence (AI) increasingly permeating various aspects of society,
including healthcare, the adoption of the Transformers neural network architecture is rapidly …

Data-driven computational approaches have evolved to enable extraction of information
from medical images with reliability, accuracy, and speed, which is already transforming …

Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid
spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic …

A key component to the success of deep learning is the availability of massive amounts of
training data. Building and annotating large datasets for solving medical image classification …

Background and Objective Transformers, which have been widely recognized as state-of-the-
art tools in natural language processing (NLP), have also come to be recognized for their …