AIMC Topic: Deep Learning

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Advancing Automatic Gastritis Diagnosis: An Interpretable Multilabel Deep Learning Framework for the Simultaneous Assessment of Multiple Indicators.

The American journal of pathology
The evaluation of morphologic features, such as inflammation, gastric atrophy, and intestinal metaplasia, is crucial for diagnosing gastritis. However, artificial intelligence analysis for nontumor diseases like gastritis is limited. Previous deep le...

SVPath: A Deep Learning Tool for Analysis of Stria Vascularis from Histology Slides.

Journal of the Association for Research in Otolaryngology : JARO
INTRODUCTION: The stria vascularis (SV) may have a significant role in various otologic pathologies. Currently, researchers manually segment and analyze the stria vascularis to measure structural atrophy. Our group developed a tool, SVPath, that uses...

Deep learning for temporomandibular joint arthropathies: A systematic review and meta-analysis.

Journal of oral rehabilitation
BACKGROUND AND OBJECTIVE: The accurate diagnosis of temporomandibular disorders continues to be a challenge, despite the existence of internationally agreed-upon diagnostic criteria. The purpose of this study is to review applications of deep learnin...

JOINEDTrans: Prior guided multi-task transformer for joint optic disc/cup segmentation and fovea detection.

Computers in biology and medicine
Deep learning-based image segmentation and detection models have largely improved the efficiency of analyzing retinal landmarks such as optic disc (OD), optic cup (OC), and fovea. However, factors including ophthalmic disease-related lesions and low ...

Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning.

Nature medicine
Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are state-of-the-art data-driven mea...

Deep learning-based super-resolution of structural brain MRI at 1.5 T: application to quantitative volume measurement.

Magma (New York, N.Y.)
OBJECTIVE: This study investigated the feasibility of using deep learning-based super-resolution (DL-SR) technique on low-resolution (LR) images to generate high-resolution (HR) MR images with the aim of scan time reduction. The efficacy of DL-SR was...

Deep learning for automatic facial detection and recognition in Japanese macaques: illuminating social networks.

Primates; journal of primatology
Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for ...

[New Method of Paired Comparison for Improved Observer Shortage Using Deep Learning Models].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: The aim of this study was to validate the potential of substituting an observer in a paired comparison with a deep-learning observer.

Missing Wedge Completion via Unsupervised Learning with Coordinate Networks.

International journal of molecular sciences
Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, whi...