AIMC Topic: Deep Learning

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Patch-based classification of gallbladder wall vascularity from laparoscopic images using deep learning.

International journal of computer assisted radiology and surgery
PURPOSE: In this study, we propose a deep learning approach for assessment of gallbladder (GB) wall vascularity from images of laparoscopic cholecystectomy (LC). Difficulty in the visualization of GB wall vessels may be the result of fatty infiltrati...

Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography.

European journal of radiology
INTRODUCTION: Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and ...

Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL.

Scientific reports
We aimed to classify early normal-tension glaucoma (NTG) and glaucoma suspect (GS) using Bruch's membrane opening-minimum rim width (BMO-MRW), peripapillary retinal nerve fiber layer (RNFL), and the color classification of RNFL based on a deep-learni...

An Efficient Deep Learning Based Method for Speech Assessment of Mandarin-Speaking Aphasic Patients.

IEEE journal of biomedical and health informatics
Speech assessment is an important part of the rehabilitation process for patients with aphasia (PWA). Mandarin speech lucidity features such as articulation, fluency, and tone influence the meaning of the spoken utterance and overall speech clarity. ...

Single Volume Image Generator and Deep Learning-Based ASD Classification.

IEEE journal of biomedical and health informatics
Autism spectrum disorder (ASD) is an intricate neuropsychiatric brain disorder characterized by social deficits and repetitive behaviors. Deep learning approaches have been applied in clinical or behavioral identification of ASD; most erstwhile model...

Innovative deep matching algorithm for stock portfolio selection using deep stock profiles.

PloS one
Construction of a reliable stock portfolio remains an open issue in quantitative investment. Multiple machine learning models have been trained for stock portfolio selection, but their practical applicability remains limited due to the challenges pos...

LV-GAN: A deep learning approach for limited-view optoacoustic imaging based on hybrid datasets.

Journal of biophotonics
The optoacoustic imaging (OAI) methods are rapidly evolving for resolving optical contrast in medical imaging applications. In practice, measurement strategies are commonly implemented under limited-view conditions due to oversized image objectives o...

NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images.

Computers in biology and medicine
The nuclei segmentation of hematoxylin and eosin (H&E) stained histopathology images is an important prerequisite in designing a computer-aided diagnostics (CAD) system for cancer diagnosis and prognosis. Automated nuclei segmentation methods enable ...