Attention-based generative adversarial network in medical imaging: A narrative review

J Zhao, X Hou, M Pan, H Zhang - Computers in Biology and Medicine, 2022 - Elsevier
As a popular probabilistic generative model, generative adversarial network (GAN) has
been successfully used not only in natural image processing, but also in medical image …

When medical images meet generative adversarial network: recent development and research opportunities

X Li, Y Jiang, JJ Rodriguez-Andina, H Luo… - Discover Artificial …, 2021 - Springer
Deep learning techniques have promoted the rise of artificial intelligence (AI) and performed
well in computer vision. Medical image analysis is an important application of deep learning …

A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded

KB Ozyoruk, S Can, B Darbaz, K Başak… - Nature Biomedical …, 2022 - nature.com
Histological artefacts in cryosectioned tissue can hinder rapid diagnostic assessments
during surgery. Formalin-fixed and paraffin-embedded (FFPE) tissue provides higher quality …

SelfVIO: Self-supervised deep monocular Visual–Inertial Odometry and depth estimation

Y Almalioglu, M Turan, MRU Saputra, PPB De Gusmão… - Neural Networks, 2022 - Elsevier
In the last decade, numerous supervised deep learning approaches have been proposed for
visual–inertial odometry (VIO) and depth map estimation, which require large amounts of …

VR-Caps: a virtual environment for capsule endoscopy

K İncetan, IO Celik, A Obeid, GI Gokceler… - Medical image …, 2021 - Elsevier
Current capsule endoscopes and next-generation robotic capsules for diagnosis and
treatment of gastrointestinal diseases are complex cyber-physical platforms that must …

Deep learning in biomedical optics

L Tian, B Hunt, MAL Bell, J Yi, JT Smith… - Lasers in surgery …, 2021 - Wiley Online Library
This article reviews deep learning applications in biomedical optics with a particular
emphasis on image formation. The review is organized by imaging domains within …

Towards automatic recognition of pure and mixed stones using intra‐operative endoscopic digital images

V Estrade, M Daudon, E Richard, JC Bernhard… - BJU …, 2022 - Wiley Online Library
Objective To assess automatic computer‐aided in situ recognition of the morphological
features of pure and mixed urinary stones using intra‐operative digital endoscopic images …

Assessment of narrow band imaging algorithm for video capsule endoscopy based on decorrelated color space for esophageal cancer

KY Yang, YJ Fang, R Karmakar, A Mukundan, YM Tsao… - Cancers, 2023 - mdpi.com
Simple Summary Video capsule endoscopy (VCE) is a small, patient-friendly tool used for
medical imaging, but it lacks narrow band imaging (NBI), which is crucial for detecting …

Generating synthetic contrast enhancement from non-contrast chest computed tomography using a generative adversarial network

JW Choi, YJ Cho, JY Ha, SB Lee, S Lee, YH Choi… - Scientific reports, 2021 - nature.com
This study aimed to evaluate a deep learning model for generating synthetic contrast-
enhanced CT (sCECT) from non-contrast chest CT (NCCT). A deep learning model was …

E-SEVSR-Edge Guided Stereo Endoscopic Video Super-Resolution

M Hayat, S Aramvith - IEEE Access, 2024 - ieeexplore.ieee.org
Integrating Stereo Imaging technology into medical diagnostics and surgeries marks a
significant revolution in medical sciences. This advancement gives surgeons and physicians …