This survey aims to identify commonly used methods, datasets, future trends, knowledge
gaps, constraints, and limitations in the field to provide an overview of current solutions used …

Deep learning (DL) has driven innovation in the field of computational imaging. One of its
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …

Synthetic data in machine learning for medicine and healthcare | Nature Biomedical Engineering
Skip to main content Thank you for visiting nature.com. You are using a browser version with …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Nuclei mymargin segmentation is a fundamental task for various computational pathology
applications including nuclei morphology analysis, cell type classification, and cancer …

Estimating a scene reconstruction and the camera motion from in-body videos is challenging
due to several factors, eg the deformation of in-body cavities or the lack of texture. In this …

Screening colonoscopy is an important clinical application for several 3D computer vision
techniques, including depth estimation, surface reconstruction, and missing region …

Abstract Background and Objective An accurate lateral cephalometric analysis is vital in
orthodontic diagnosis. Identification of anatomic landmarks on lateral cephalograms is …

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

This article reviews deep learning applications in biomedical optics with a particular
emphasis on image formation. The review is organized by imaging domains within …