AIMC Topic: Deep Learning

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Linguistic generalization and compositionality in modern artificial neural networks.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
In the last decade, deep artificial neural networks have achieved astounding performance in many natural language-processing tasks. Given the high productivity of language, these models must possess effective generalization abilities. It is widely as...

Evolving artificial neural networks with feedback.

Neural networks : the official journal of the International Neural Network Society
Neural networks in the brain are dominated by sometimes more than 60% feedback connections, which most often have small synaptic weights. Different from this, little is known how to introduce feedback into artificial neural networks. Here we use tran...

Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach.

Sensors (Basel, Switzerland)
Visual stimuli from photographs and artworks raise corresponding emotional responses. It is a long process to prove whether the emotions that arise from photographs and artworks are different or not. We answer this question by employing electroenceph...

Highway crash detection and risk estimation using deep learning.

Accident; analysis and prevention
Crash Detection is essential in providing timely information to traffic management centers and the public to reduce its adverse effects. Prediction of crash risk is vital for avoiding secondary crashes and safeguarding highway traffic. For many years...

Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy.

Physics in medicine and biology
Accurately predicting treatment outcome is crucial for creating personalized treatment plans and follow-up schedules. Electronic health records (EHRs) contain valuable patient-specific information that can be leveraged to improve outcome prediction. ...

Mitral Annulus Segmentation Using Deep Learning in 3-D Transesophageal Echocardiography.

IEEE journal of biomedical and health informatics
3D Transesophageal Echocardiography is an excellent tool for evaluating the mitral valve and is also well suited for guiding cardiac interventions. We introduce a fully automatic method for mitral annulus segmentation in 3D Transesophageal Echocardio...

A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients.

International journal of molecular sciences
Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs' molecu...

Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks.

Scientific reports
Accurate identification of bone surface modifications (BSM) is crucial for the taphonomic understanding of archaeological and paleontological sites. Critical interpretations of when humans started eating meat and animal fat or when they started using...

A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images.

BMC medical informatics and decision making
BACKGROUND: The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmen...

Channel-spatial attention network for fewshot classification.

PloS one
Learning a powerful representation for a class with few labeled samples is a challenging problem. Although some state-of-the-art few-shot learning algorithms perform well based on meta-learning, they only focus on novel network architecture and fail ...