AIMC Topic: Deep Learning

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Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications.

IEEE transactions on pattern analysis and machine intelligence
The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques ...

Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning With Deep Belief Networks.

IEEE journal of biomedical and health informatics
This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A three-layer deep belief network (DBN) with two hidden layers and one visible layer w...

3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
High-grade glioma is the most aggressive and severe brain tumor that leads to death of almost 50% patients in 1-2 years. Thus, accurate prognosis for glioma patients would provide essential guidelines for their treatment planning. Conventional surviv...

Deep Fish.

SLAS discovery : advancing life sciences R & D
Zebrafish ( Danio rerio) is an important vertebrate model organism in biomedical research, especially suitable for morphological screening due to its transparent body during early development. Deep learning has emerged as a dominant paradigm for data...

Predicting the Absorption Potential of Chemical Compounds Through a Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics
The human colorectal carcinoma cell line (Caco-2) is a commonly used in-vitro test that predicts the absorption potential of orally administered drugs. In-silico prediction methods, based on the Caco-2 assay data, may increase the effectiveness of th...

PoseR: a deep learning toolbox for classifying animal behaviour.

Open biology
The actions of animals provide a window into how their minds work. Recent advances in deep learning are providing powerful approaches to recognize patterns of animal movement from video recordings using markerless pose estimation models. Current meth...

Shortcomings of deep learning for distributional predictors: a note.

Biostatistics (Oxford, England)
A number of domains in biomedical research use data with a large number of predictors all representing the same type of measurement. Often, an important summary is the within-person distribution of these predictors. Here we focus on settings where th...

[Evaluation of an interpretable 12-lead ECG automatic diagnosis model based on deep feature fusion].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVES: To enhance the accuracy and reliability of 12-lead electrocardiogram (ECG) automatic diagnosis. METHODS: Herein we propose a 12-lead ECG automatic diagnosis model based on deep feature fusion (MRHL-ECGNet), which consists of a multi-scale...

[Application and research progress of artificial intelligence in the diagnosis and treatment of rare lung diseases].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases
Rare lung diseases are a group of diseases characterized by significant clinical heterogeneity, challenging diagnosis and treatment processes, and diverse underlying causes. Due to their uncommon symptoms and limited awareness among healthcare provid...

Bridging AI and biology: Foundation models meet human physiology and disease.

Med (New York, N.Y.)
Translation of foundation models from benchmarks to clinical impact has been slow, revealing a fundamental limitation: correlation-based predictions from models trained on observational data miss causal pathways that determine disease course and trea...