AIMC Topic: Deep Learning

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A comparative study of the inter-observer variability on Gleason grading against Deep Learning-based approaches for prostate cancer.

Computers in biology and medicine
BACKGROUND: Among all the cancers known today, prostate cancer is one of the most commonly diagnosed in men. With modern advances in medicine, its mortality has been considerably reduced. However, it is still a leading type of cancer in terms of deat...

First Report On Physician Assessment and Clinical Acceptability of Custom-Retrained Artificial Intelligence Models for Clinical Target Volume and Organs-at-Risk Auto-Delineation for Postprostatectomy Patients.

Practical radiation oncology
PURPOSE: To assess the clinical acceptability of a commercial deep-learning-based auto-segmentation (DLAS) prostate model that was retrained using institutional data for delineation of the clinical target volume (CTV) and organs-at-risk (OARs) for po...

Coupling analysis of heart rate variability and cortical arousal using a deep learning algorithm.

PloS one
Frequent cortical arousal is associated with cardiovascular dysfunction among people with sleep-disordered breathing. Changes in heart rate variability (HRV) can represent pathological conditions associated with autonomic nervous system dysfunction. ...

Deep learning for detection of age-related macular degeneration: A systematic review and meta-analysis of diagnostic test accuracy studies.

PloS one
OBJECTIVE: To evaluate the diagnostic accuracy of deep learning algorithms to identify age-related macular degeneration and to explore factors impacting the results for future model training.

Hazard source detection of longitudinal tearing of conveyor belt based on deep learning.

PloS one
Belt tearing is the main safety accident of belt conveyor. The main cause of tearing is the doped bolt and steel in the conveying belt. In this paper, the bolt and steel are identified as the Hazard source of tear. In this paper, bolt and steel are d...

Fully-automated detection of small bowel carcinoid tumors in CT scans using deep learning.

Medical physics
BACKGROUND: Small bowel carcinoid tumor is a rare neoplasm and increasing in incidence. Patients with small bowel carcinoid tumors often experience long delays in diagnosis due to the vague symptoms, slow growth of tumors, and lack of clinician aware...

Dose reduction and image enhancement in micro-CT using deep learning.

Medical physics
BACKGROUND: In preclinical settings, micro-computed tomography (CT) provides a powerful tool to acquire high resolution anatomical images of rodents and offers the advantage to in vivo non-invasively assess disease progression and therapy efficacy. M...

DBCU-Net: deep learning approach for segmentation of coronary angiography images.

The international journal of cardiovascular imaging
Coronary angiography (CAG) is the "gold standard" for diagnosing coronary artery disease (CAD). However, due to the limitation of current imaging methods, the CAG image has low resolution and poor contrast with a lot of artifacts and noise, which mak...

Comparison of deep learning models for digital H&E staining from unpaired label-free multispectral microscopy images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: This paper presents the quantitative comparison of three generative models of digital staining, also known as virtual staining, in H&E modality (i.e., Hematoxylin and Eosin) that are applied to 5 types of breast tissue. More...

Acquisition time reduction in pediatric Tc-DMSA planar imaging using deep learning.

Journal of applied clinical medical physics
PURPOSE: Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric Tc-dimercaptosuccinic acid (DMSA) scintigraphy. The aim of this study was to evaluate the performance of predicted full-acquisition-time imag...