AIMC Topic: Deep Learning

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An Enhanced Region Proposal Network for object detection using deep learning method.

PloS one
Faster Region-based Convolutional Network (Faster R-CNN) is a state-of-the-art object detection method. However, the object detection effect of Faster R-CNN is not good based on the Region Proposal Network (RPN). Inspired by RPN of Faster R-CNN, we p...

Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis.

The British journal of dermatology
BACKGROUND: Application of deep-learning technology to skin cancer classification can potentially improve the sensitivity and specificity of skin cancer screening, but the number of training images required for such a system is thought to be extremel...

Multi-stage optimization of a deep model: A case study on ground motion modeling.

PloS one
In this study, a multi-stage optimization procedure is proposed to develop deep neural network models which results in a powerful deep learning pipeline called intelligent deep learning (iDeepLe). The proposed pipeline is then evaluated by a challeng...

A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites.

International journal of molecular sciences
Protein hydroxylation is one type of post-translational modifications (PTMs) playing critical roles in human diseases. It is known that protein sequence contains many uncharacterized residues of proline and lysine. The question that needs to be answe...

Deep Learning in Drug Discovery and Medicine; Scratching the Surface.

Molecules (Basel, Switzerland)
The practice of medicine is ever evolving. Diagnosing disease, which is often the first step in a cure, has seen a sea change from the discerning hands of the neighborhood physician to the use of sophisticated machines to use of information gleaned f...

Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network.

PloS one
Several computer aided diagnosis (CAD) systems have been developed for mammography. They are widely used in certain countries such as the U.S. where mammography studies are conducted more frequently; however, they are not yet globally employed for cl...

Deep learning nuclei detection: A simple approach can deliver state-of-the-art results.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: Deep convolutional neural networks have become a widespread tool for the detection of nuclei in histopathology images. Many implementations share a basic approach that includes generation of an intermediate map indicating the presence of ...

Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study.

The Lancet. Respiratory medicine
BACKGROUND: Based on international diagnostic guidelines, high-resolution CT plays a central part in the diagnosis of fibrotic lung disease. In the correct clinical context, when high-resolution CT appearances are those of usual interstitial pneumoni...