AIMC Topic: Deep Learning

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Multiple instance learning detects peripheral arterial disease from high-resolution color fundus photography.

Scientific reports
Peripheral arterial disease (PAD) is caused by atherosclerosis and is a common disease of the elderly leading to excess morbidity and mortality. Early PAD diagnosis is important, as the only available causal therapy is addressing risk factors like sm...

Deep learning-based school attendance prediction for autistic students.

Scientific reports
Autism Spectrum Disorder is a neurodevelopmental disorder characterized by deficits in social communication and interaction as well as the presence of repetitive, restricted patterns of behavior, interests, or activities. Many autistic students exper...

A deep learning approach to predict inter-omics interactions in multi-layer networks.

BMC bioinformatics
BACKGROUND: Despite enormous achievements in the production of high-throughput datasets, constructing comprehensive maps of interactions remains a major challenge. Lack of sufficient experimental evidence on interactions is more significant for heter...

Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia.

Acta dermato-venereologica
Since the results of basic and specific classification in male androgenetic alopecia are subjective, and trichoscopic data, such as hair density and diameter distribution, are potential quantitative indicators, the aim of this study was to develop a ...

Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things.

PloS one
To improve the teaching effect of western music history, the curriculum reform of history education needs to be promoted under the background of the Internet of Things (IoT). At first, a discussion is made on the characteristics of history course, wh...

AIME: Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments.

PLoS computational biology
In the integrative analyses of omics data, it is often of interest to extract data representation from one data type that best reflect its relations with another data type. This task is traditionally fulfilled by linear methods such as canonical corr...

A deep learning model for breast ductal carcinoma in situ classification in whole slide images.

Virchows Archiv : an international journal of pathology
The pathological differential diagnosis between breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is of pivotal importance for determining optimum cancer treatment(s) and clinical outcomes. Since conventional diagnosis by pat...

Deep Learning for Detecting Pneumothorax on Chest Radiographs after Needle Biopsy: Clinical Implementation.

Radiology
Background Accurate detection of pneumothorax on chest radiographs, the most common complication of percutaneous transthoracic needle biopsies (PTNBs), is not always easy in practice. A computer-aided detection (CAD) system may help detect pneumothor...

Overcoming challenges in extracting prescribing habits from veterinary clinics using big data and deep learning.

Australian veterinary journal
Understanding antimicrobial usage patterns and encouraging appropriate antimicrobial usage is a critical component of antimicrobial stewardship. Studies using VetCompass Australia and Natural Language Processing (NLP) have demonstrated antimicrobial ...