AIMC Topic: Deep Learning

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Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence.

Computers in biology and medicine
A deep learning (DL) network for 2D-based breast mass segmentation in unenhanced dedicated breast CT images was developed and validated, and its robustness in radiomic feature stability and diagnostic performance compared to manual annotations of mul...

Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals.

eLife
Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict e...

Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks.

Computer methods and programs in biomedicine
Breast ultrasound and computer aided diagnosis (CAD) has been used to classify tumors into benignancy or malignancy. However, conventional CAD software has some problems (such as handcrafted features are hard to design; conventional CAD systems are d...

Computer-aided tumor detection in automated breast ultrasound using a 3-D convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Automated breast ultrasound (ABUS) is a widely used screening modality for breast cancer detection and diagnosis. In this study, an effective and fast computer-aided detection (CADe) system based on a 3-D convolutional neur...

Theory of deep convolutional neural networks: Downsampling.

Neural networks : the official journal of the International Neural Network Society
Establishing a solid theoretical foundation for structured deep neural networks is greatly desired due to the successful applications of deep learning in various practical domains. This paper aims at an approximation theory of deep convolutional neur...

Deep Learning-Based Single-Cell Optical Image Studies.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Optical imaging technology that has the advantages of high sensitivity and cost-effectiveness greatly promotes the progress of nondestructive single-cell studies. Complex cellular image analysis tasks such as three-dimensional reconstruction call for...

Automated classification of normal and Stargardt disease optical coherence tomography images using deep learning.

Acta ophthalmologica
PURPOSE: Recent advances in deep learning have seen an increase in its application to automated image analysis in ophthalmology for conditions with a high prevalence. We wanted to identify whether deep learning could be used for the automated classif...

Deep pancreas segmentation with uncertain regions of shadowed sets.

Magnetic resonance imaging
Pancreas segmentation is a challenging task in medical image analysis especially for the patients with pancreatic cancer. First, the images often have poor contrast and blurred boundaries. Second, there exist large variations in gray scale, texture, ...

Deep learning for plant genomics and crop improvement.

Current opinion in plant biology
Our era has witnessed tremendous advances in plant genomics, characterized by an explosion of high-throughput techniques to identify multi-dimensional genome-wide molecular phenotypes at low costs. More importantly, genomics is not merely acquiring m...