AIMC Topic: Deep Learning

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Forecasting nitrous oxide emissions from a full-scale wastewater treatment plant using LSTM-based deep learning models.

Water research
Nitrous oxide (NO) emissions from wastewater treatment plants (WWTPs) exhibit significant seasonal variability, making accurate predictions with conventional biokinetic models difficult due to complex and poorly understood biochemical processes. This...

Enhanced NSCLC subtyping and staging through attention-augmented multi-task deep learning: A novel diagnostic tool.

International journal of medical informatics
OBJECTIVES: The objective of this study is to develop a novel multi-task learning approach with attention encoders for classifying histologic subtypes and clinical stages of non-small cell lung cancer (NSCLC), with superior performance compared to cu...

A generalised computer vision model for improved glaucoma screening using fundus images.

Eye (London, England)
IMPORTANCE: Worldwide, glaucoma is a leading cause of irreversible blindness. Timely detection is paramount yet challenging, particularly in resource-limited settings. A novel, computer vision-based model for glaucoma screening using fundus images co...

Deep Learning-Assisted Label-Free Parallel Cell Sorting with Digital Microfluidics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label-free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to...

Multi-lesion segmentation guided deep attention network for automated detection of diabetic retinopathy.

Computers in biology and medicine
Accurate multi-lesion segmentation together with automated grading on fundus images played a vital role in diagnosing and treating diabetic retinopathy (DR). Nevertheless, the intrinsic patterns of fundus lesions aggravated challenges in DR detection...

Deep learning approaches for automated classification of neonatal lung ultrasound with assessment of human-to-AI interrater agreement.

Computers in biology and medicine
Neonatal respiratory disorders pose significant challenges in clinical settings, often requiring rapid and accurate diagnostic solutions for effective management. Lung ultrasound (LUS) has emerged as a promising tool to evaluate respiratory condition...

A deep learning approach for gastroscopic manifestation recognition based on Kyoto Gastritis Score.

Annals of medicine
OBJECTIVE: The risk of gastric cancer can be predicted by gastroscopic manifestation recognition and the Kyoto Gastritis Score. This study aims to validate the applicability of AI approaches for recognizing gastroscopic manifestations according to th...

ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction.

Journal of chemical information and modeling
The rapid progression of machine learning, especially deep learning (DL), has catalyzed a new era in drug discovery, introducing innovative approaches for predicting molecular properties. Despite the many methods available for feature representation,...

CSTAN: A Deepfake Detection Network with CST Attention for Superior Generalization.

Sensors (Basel, Switzerland)
With the advancement of deepfake forgery technology, highly realistic fake faces have posed serious security risks to sensor-based facial recognition systems. Recent deepfake detection models mainly use binary classification models based on deep lear...

Using spatial video and deep learning for automated mapping of ground-level context in relief camps.

International journal of health geographics
BACKGROUND: The creation of relief camps following a disaster, conflict or other form of externality often generates additional health problems. The density of people in a highly stressed environment with questionable safe food and water access prese...