AIMC Topic: Deep Learning

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Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts.

Nature communications
Drug discovery for diseases such as Parkinson's disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and dee...

Phase function estimation from a diffuse optical image via deep learning.

Physics in medicine and biology
The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters. In recent years, machine learning methods were reported to estimate the pa...

Application of Deep Learning in Breast Cancer Imaging.

Seminars in nuclear medicine
This review gives an overview of the current state of deep learning research in breast cancer imaging. Breast imaging plays a major role in detecting breast cancer at an earlier stage, as well as monitoring and evaluating breast cancer during treatme...

Evaluation of deep learning reconstructed high-resolution 3D lumbar spine MRI.

European radiology
OBJECTIVES: To compare interobserver agreement and image quality of 3D T2-weighted fast spin echo (T2w-FSE) L-spine MRI images processed with a deep learning reconstruction (DLRecon) against standard-of-care (SOC) reconstruction, as well as against 2...

A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer.

Journal of cancer research and clinical oncology
PURPOSE: Most of Stage II/III colorectal cancer (CRC) patients can be cured by surgery alone, and only certain CRC patients benefit from adjuvant chemotherapy. Risk stratification based on deep-learning from haematoxylin and eosin (H&E) images has be...

New double decomposition deep learning methods for river water level forecasting.

The Science of the total environment
Forecasting river water levels or streamflow water levels (SWL) is vital to optimising the practical and sustainable use of available water resources. We propose a new deep learning hybrid model for SWL forecasting using convolutional neural networks...

Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge.

Medical image analysis
A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed 10 min aft...

Polysomnographic identification of anxiety and depression using deep learning.

Journal of psychiatric research
Anxiety and depression are common psychiatric conditions associated with significant morbidity and healthcare costs. Sleep is an evolutionarily conserved health state. Anxiety and depression have a bidirectional relationship with sleep. This study re...

Diagnostic accuracy of a commercially available deep-learning algorithm in supine chest radiographs following trauma.

The British journal of radiology
OBJECTIVES: Trauma chest radiographs may contain subtle and time-critical pathology. Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist prioritisation. However, few AI programs have been externally validate...