AIMC Topic: Deep Learning

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A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer.

Journal of healthcare engineering
OBJECT: Pathologic prediction of prostate cancer can be made by predicting the patient's prostate metastasis prior to surgery based on biopsy information. Because biopsy variables associated with pathology have uncertainty regarding individual patien...

Position-aware deep multi-task learning for drug-drug interaction extraction.

Artificial intelligence in medicine
OBJECTIVE: A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prev...

Deep Spatiality: Unsupervised Learning of Spatially-Enhanced Global and Local 3D Features by Deep Neural Network with Coupled Softmax.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The discriminability of Bag-of-Words representations can be increased via encoding the spatial relationship among virtual words on 3D shapes. However, this encoding task involves several issues, including arbitrary mesh resolutions, irregular vertex ...

An application of cascaded 3D fully convolutional networks for medical image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical st...

Detecting and classifying lesions in mammograms with Deep Learning.

Scientific reports
In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimat...

MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

Proteins
Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neura...

A deep learning approach for pose estimation from volumetric OCT data.

Medical image analysis
Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micromet...

Medical Image Synthesis with Deep Convolutional Adversarial Networks.

IEEE transactions on bio-medical engineering
Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great...