AIMC Topic: Deep Learning

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A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data.

IEEE journal of biomedical and health informatics
Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involve...

Extracting Cause of Death From Verbal Autopsy With Deep Learning Interpretable Methods.

IEEE journal of biomedical and health informatics
The international standard to ascertain the cause of death is medical certification. However, in many low and middle-income countries, the majority of deaths occur outside of health facilities. In these cases, Verbal Autopsy (VA), the narrative provi...

ncRFP: A Novel end-to-end Method for Non-Coding RNAs Family Prediction Based on Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Evidence has accumulated enough to prove non-coding RNAs (ncRNAs) play important roles in cellular biological processes and disease pathogenesis. High throughput techniques have produced a large number of ncRNAs whose function remains unknown. Since ...

DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines.

IEEE/ACM transactions on computational biology and bioinformatics
High-throughput screening technologies have provided a large amount of drug sensitivity data for a panel of cancer cell lines and hundreds of compounds. Computational approaches to analyzing these data can benefit anticancer therapeutics by identifyi...

A Cascade of 2.5D CNN and Bidirectional CLSTM Network for Mitotic Cell Detection in 4D Microscopy Image.

IEEE/ACM transactions on computational biology and bioinformatics
Mitosis detection is one of the challenging steps in biomedical imaging research, which can be used to observe the cell behavior. Most of the already existing methods that are applied in detecting mitosis usually contain many nonmitotic events (norma...

Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings.

Neural networks : the official journal of the International Neural Network Society
Respiration is an essential and primary mechanism for speech production. We first inhale and then produce speech while exhaling. When we run out of breath, we stop speaking and inhale. Though this process is involuntary, speech production involves a ...

Two-stage deep learning for accelerated 3D time-of-flight MRA without matched training data.

Medical image analysis
Time-of-flight magnetic resonance angiography (TOF-MRA) is one of the most widely used non-contrast MR imaging methods to visualize blood vessels, but due to the 3-D volume acquisition highly accelerated acquisition is necessary. Accordingly, high qu...

Deep transfer learning can be used for the detection of hip joints in pelvis radiographs and the classification of their hip dysplasia status.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Reports of machine learning implementations in veterinary imaging are infrequent but changes in machine learning architecture and access to increased computing power will likely prompt increased interest. This diagnostic accuracy study describes a pa...

An integrated deep learning and dynamic programming method for predicting tumor suppressor genes, oncogenes, and fusion from PDB structures.

Computers in biology and medicine
Mutations in proto-oncogenes (ONGO) and the loss of regulatory function of tumor suppression genes (TSG) are the common underlying mechanism for uncontrolled tumor growth. While cancer is a heterogeneous complex of distinct diseases, finding the pote...

Deep learning classification of lipid droplets in quantitative phase images.

PloS one
We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing,...