AIMC Topic: Deep Learning

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Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning.

Medical image analysis
The COVID-19 pandemic is causing a major outbreak in more than 150 countries around the world, having a severe impact on the health and life of many people globally. One of the crucial step in fighting COVID-19 is the ability to detect the infected p...

Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images.

Ultrasound in medicine & biology
Incorporating human domain knowledge for breast tumor diagnosis is challenging because shape, boundary, curvature, intensity or other common medical priors vary significantly across patients and cannot be employed. This work proposes a new approach t...

Multi-site household waste generation forecasting using a deep learning approach.

Waste management (New York, N.Y.)
Forecasting household waste generation using traditional methods is particularly challenging due to its high variability and uncertainty. Unlike studies that forecast waste generation at municipal or country levels, household data can present rapid s...

Deep Learning-based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs.

Radiology
Background It is uncertain whether a deep learning-based automatic detection algorithm (DLAD) for identifying malignant nodules on chest radiographs will help diagnose lung cancers. Purpose To evaluate the efficacy of using a DLAD in observer perform...

Neurolight: A Deep Learning Neural Interface for Cortical Visual Prostheses.

International journal of neural systems
Visual neuroprosthesis, that provide electrical stimulation along several sites of the human visual system, constitute a potential tool for vision restoration for the blind. Scientific and technological progress in the fields of neural engineering an...

A hybrid unsupervised-Deep learning tandem for electrooculography time series analysis.

PloS one
Medical data are often tricky to get mined for patterns even by the generally demonstrated successful modern methodologies of deep learning. This paper puts forward such a medical classification task, where patient registers of two of the categories ...

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning.

Gut
OBJECTIVE: Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), hi...

Cortical surface registration using unsupervised learning.

NeuroImage
Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface propert...

A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy.

International journal of computer assisted radiology and surgery
PURPOSE: This paper addresses the detection of the clinical target volume (CTV) in transrectal ultrasound (TRUS) image-guided intraoperative for permanent prostate brachytherapy. Developing a robust and automatic method to detect the CTV on intraoper...

Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images.

Radiation oncology (London, England)
BACKGROUND: Impaired function of masticatory muscles will lead to trismus. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed...