AIMC Topic: Deep Learning

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Cancer classification based on chromatin accessibility profiles with deep adversarial learning model.

PLoS computational biology
Given the complexity and diversity of the cancer genomics profiles, it is challenging to identify distinct clusters from different cancer types. Numerous analyses have been conducted for this propose. Still, the methods they used always do not direct...

CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks.

PloS one
BACKGROUND: Pneumothorax can lead to a life-threatening emergency. The experienced radiologists can offer precise diagnosis according to the chest radiographs. The localization of the pneumothorax lesions will help to quickly diagnose, which will be ...

List-wise learning to rank biomedical question-answer pairs with deep ranking recursive autoencoders.

PloS one
Biomedical question answering (QA) represents a growing concern among industry and academia due to the crucial impact of biomedical information. When mapping and ranking candidate snippet answers within relevant literature, current QA systems typical...

Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information.

Biomolecules
Image segmentation is the pixel-by-pixel detection of objects, which is the most challenging but informative in the fundamental tasks of machine learning including image classification and object detection. Pixel-by-pixel segmentation is required to ...

Echo Memory-Augmented Network for time series classification.

Neural networks : the official journal of the International Neural Network Society
Echo State Networks (ESNs) are efficient recurrent neural networks (RNNs) which have been successfully applied to time series modeling tasks. However, ESNs are unable to capture the history information far from the current time step, since the echo s...

Artificial intelligence solution to classify pulmonary nodules on CT.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to create an algorithm to detect and classify pulmonary nodules in two categories based on their volume greater than 100 mm or not, using machine learning and deep learning techniques.

Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs.

Scientific reports
Computer-assisted analysis of dental radiograph in dentistry is getting increasing attention from the researchers in recent years. This is mainly because it can successfully reduce human-made error due to stress, fatigue or lack of experience. Furthe...

Learning to encode cellular responses to systematic perturbations with deep generative models.

NPJ systems biology and applications
Cellular signaling systems play a vital role in maintaining homeostasis when a cell is exposed to different perturbations. Components of the systems are organized as hierarchical networks, and perturbing different components often leads to transcript...

Deep learning-enabled multi-organ segmentation in whole-body mouse scans.

Nature communications
Whole-body imaging of mice is a key source of information for research. Organ segmentation is a prerequisite for quantitative analysis but is a tedious and error-prone task if done manually. Here, we present a deep learning solution called AIMOS that...