AIMC Topic: Deep Learning

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NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework.

IEEE transactions on cybernetics
Brain-computer interfaces (BCIs) have been widely employed to identify and estimate a user's intention to trigger a robotic device by decoding motor imagery (MI) from an electroencephalogram (EEG). However, developing a BCI system driven by MI relate...

Multi-Perspective Hierarchical Deep-Fusion Learning Framework for Lung Nodule Classification.

Sensors (Basel, Switzerland)
Lung cancer is the leading cancer type that causes mortality in both men and women. Computer-aided detection (CAD) and diagnosis systems can play a very important role for helping physicians with cancer treatments. This study proposes a hierarchical ...

Automatic Classification Service System for Citrus Pest Recognition Based on Deep Learning.

Sensors (Basel, Switzerland)
Plant diseases are a major cause of reduction in agricultural output, which leads to severe economic losses and unstable food supply. The citrus plant is an economically important fruit crop grown and produced worldwide. However, citrus plants are ea...

Graph based multi-scale neighboring topology deep learning for kidney and tumor segmentation.

Physics in medicine and biology
Effective learning and modelling of spatial and semantic relations between image regions in various ranges are critical yet challenging in image segmentation tasks.We propose a novel deep graph reasoning model to learn from multi-order neighborhood t...

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues.

Communications biology
Upcoming technologies enable routine collection of highly multiplexed (20-60 channel), subcellular resolution images of mammalian tissues for research and diagnosis. Extracting single cell data from such images requires accurate image segmentation, a...

Deep multiple instance learning for predicting chemotherapy response in non-small cell lung cancer using pretreatment CT images.

Scientific reports
The individual prognosis of chemotherapy is quite different in non-small cell lung cancer (NSCLC). There is an urgent need to precisely predict and assess the treatment response. To develop a deep multiple-instance learning (DMIL) based model for pre...

Using model explanations to guide deep learning models towards consistent explanations for EHR data.

Scientific reports
It has been shown that identical deep learning (DL) architectures will produce distinct explanations when trained with different hyperparameters that are orthogonal to the task (e.g. random seed, training set order). In domains such as healthcare and...

Deep learning system for paddy plant disease detection and classification.

Environmental monitoring and assessment
Automatic detection and analysis of rice crop diseases is widely required in the farming industry, which can be utilized to avoid squandering financial and other resources, reduce yield losses, and improve treatment efficiency, resulting in healthier...

Capturing the potential energy landscape of large size molecular clusters from atomic interactions up to a 4-body system using deep learning.

Physical chemistry chemical physics : PCCP
Exploring the structure and properties of molecular clusters with accuracy using the methods is a resource intensive task due to the increasing cost of the methods and the number of distinct conformers as the size increases. The energy landscape of...

Automated calibration system for length measurement of lateral cephalometry based on deep learning.

Physics in medicine and biology
. Cephalometric analysis has been significantly facilitated by artificial intelligence (AI) in recent years. For digital cephalograms, linear measurements are conducted based on the length calibration process, which has not been automatized in curren...