Deep learning has received extensive research interest in develo** new medical image
processing algorithms, and deep learning based models have been remarkably successful …

Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …

This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …

Brain tumor segmentation from MRIs has always been a challenging task for radiologists,
therefore, an automatic and generalized system to address this task is needed. Among all …

Successful training of convolutional neural networks (CNNs) requires a substantial amount
of data. With small datasets, networks generalize poorly. Data Augmentation techniques …

Early breast cancer detection is one of the most important issues that need to be addressed
worldwide as it can help increase the survival rate of patients. Mammograms have been …

Medical images differ from natural images in significantly higher resolutions and smaller
regions of interest. Because of these differences, neural network architectures that work well …

Research in the medical imaging field using deep learning approaches has become
progressively contingent. Scientific findings reveal that supervised deep learning methods' …

In this work we design an end-to-end deep learning architecture for predicting, on Chest X-
rays images (CXR), a multi-regional score conveying the degree of lung compromise in …

Image registration is a fundamental task in multiple medical image analysis applications.
With the advent of deep learning, there have been significant advances in algorithmic …