AIMC Topic: Deep Learning

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A hybrid Neural Network Model for Joint Prediction of Presence and Period Assertions of Medical Events in Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
In this paper, we propose a novel neural network architecture for clinical text mining. We formulate this hybrid neural network model (HNN), composed of recurrent neural network and deep residual network, to jointly predict the presence and period as...

A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Gleason grading of histological images is important in risk assessment and treatment planning for prostate cancer patients. Much research has been done in classifying small homogeneous cancer regions within histological images. However, semi-supervis...

Differential Data Augmentation Techniques for Medical Imaging Classification Tasks.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Data augmentation is an essential part of training discriminative Convolutional Neural Networks (CNNs). A variety of augmentation strategies, including horizontal flips, random crops, and principal component analysis (PCA), have been proposed and sho...

Classifying Acute Ischemic Stroke Onset Time using Deep Imaging Features.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Models have been developed to predict stroke outcomes (e.g., mortality) in attempt to provide better guidance for stroke treatment. However, there is little work in developing classification models for the problem of unknown time-since-stroke (TSS), ...

Inferring Clinical Correlations from EEG Reports with Deep Neural Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Successful diagnosis and management of neurological dysfunction relies on proper communication between the neurologist and the primary physician (or other specialists). Because this communication is documented within medical records, the ability to a...

Deep Learning Solutions for Classifying Patients on Opioid Use.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Opioid analgesics, as commonly prescribed medications used for relieving pain in patients, are especially prevalent in US these years. However, an increasing amount of opioid misuse and abuse have caused lots of consequences. Researchers and clinicia...

Visual Kinship Recognition of Families in the Wild.

IEEE transactions on pattern analysis and machine intelligence
We present the largest database for visual kinship recognition, Families In the Wild (FIW), with over 13,000 family photos of 1,000 family trees with 4-to-38 members. It took only a small team to build FIW with efficient labeling tools and work-flow....

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

Computers in biology and medicine
Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framewo...

Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature.

Journal of biomedical semantics
BACKGROUND: Automatic identification of term variants or acceptable alternative free-text terms for gene and protein names from the millions of biomedical publications is a challenging task. Ontologies, such as the Cardiovascular Disease Ontology (CV...

Why Deep Learning Is Changing the Way to Approach NGS Data Processing: A Review.

IEEE reviews in biomedical engineering
Nowadays, big data analytics in genomics is an emerging research topic. In fact, the large amount of genomics data originated by emerging next-generation sequencing (NGS) techniques requires more and more fast and sophisticated algorithms. In this co...