AIMC Topic: Deep Learning

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Carbon Dot Blinking Fingerprint Uncovers Native Membrane Receptor Organizations via Deep Learning.

Analytical chemistry
Oligomeric organization of G protein-coupled receptors is proposed to regulate receptor signaling and function, yet rapid and precise identification of the oligomeric status especially for native receptors on a cell membrane remains an outstanding ch...

Detecting visually significant cataract using retinal photograph-based deep learning.

Nature aging
Age-related cataracts are the leading cause of visual impairment among older adults. Many significant cases remain undiagnosed or neglected in communities, due to limited availability or accessibility to cataract screening. In the present study, we r...

Automated human cell classification in sparse datasets using few-shot learning.

Scientific reports
Classifying and analyzing human cells is a lengthy procedure, often involving a trained professional. In an attempt to expedite this process, an active area of research involves automating cell classification through use of deep learning-based techni...

A Simple and Efficient Deep Learning-Based Framework for Automatic Fruit Recognition.

Computational intelligence and neuroscience
Accurate detection and recognition of various kinds of fruits and vegetables by using the artificial intelligence (AI) approach always remain a challenging task due to similarity between various types of fruits and challenging environments such as li...

Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Age-related macular degeneration (AMD) is the leading cause of vision loss. Some patients experience vision loss over a delayed timeframe, others at a rapid pace. Physicians analyze time-of-visit fundus photographs to predict patient risk of developi...

DL4Burn: Burn Surgical Candidacy Prediction using Multimodal Deep Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Burn wounds are most commonly evaluated through visual inspection to determine surgical candidacy, taking into account burn depth and individualized patient factors. This process, though cost effective, is subjective and varies by provider experience...

Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge burnouts and de...

Comparing Deep Learning and Conventional Machine Learning Models for Predicting Mental Illness from History of Present Illness Notations.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Mental illness, a serious problem across the globe, requires multi-pronged solutions including effective computational models to predict illness. Mental illness diagnosis is complicated by the pronounced sharing of symptoms and mutual pre-disposition...

Inferring pediatric knee skeletal maturity from MRI using deep learning.

Skeletal radiology
PURPOSE: Many children who undergo MR of the knee to evaluate traumatic injury may not undergo a separate dedicated evaluation of their skeletal maturity, and we wished to investigate how accurately skeletal maturity could be automatically inferred f...

Deep learning identifies brain structures that predict cognition and explain heterogeneity in cognitive aging.

NeuroImage
Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adult...