AIMC Topic: Deep Learning

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Monitoring activity index and behaviors of cage-free hens with advanced deep learning technologies.

Poultry science
Chickens' behaviors and activities are important information for managing animal health and welfare in commercial poultry houses. In this study, convolutional neural networks (CNN) models were developed to monitor the chicken activity index. A datase...

Effective descriptor extraction strategies for correspondence matching in coronary angiography images.

Scientific reports
The importance of 3D reconstruction of coronary arteries using multiple coronary angiography (CAG) images has been increasingly recognized in the field of cardiovascular disease management. This process relies on the camera matrix's optimization, nee...

Long-term trend forecast of chlorophyll-a concentration over eutrophic lakes based on time series decomposition and deep learning algorithm.

The Science of the total environment
Long-term trend forecast of chlorophyll-a concentration (Chla) holds significant implications for eutrophication management and pollution control planning on lakes, especially under the background of climate change. However, it is a challenging task ...

Metadata-enhanced contrastive learning from retinal optical coherence tomography images.

Medical image analysis
Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-...

Neuro-XAI: Explainable deep learning framework based on deeplabV3+ and bayesian optimization for segmentation and classification of brain tumor in MRI scans.

Journal of neuroscience methods
The prevalence of brain tumor disorders is currently a global issue. In general, radiography, which includes a large number of images, is an efficient method for diagnosing these life-threatening disorders. The biggest issue in this area is that it t...

Assessment of left ventricular wall thickness and dimension: accuracy of a deep learning model with prediction uncertainty.

The international journal of cardiovascular imaging
Left ventricular (LV) geometric patterns aid clinicians in the diagnosis and prognostication of various cardiomyopathies. The aim of this study is to assess the accuracy and reproducibility of LV dimensions and wall thickness using deep learning (DL)...

Physics-informed deep generative learning for quantitative assessment of the retina.

Nature communications
Disruption of retinal vasculature is linked to various diseases, including diabetic retinopathy and macular degeneration, leading to vision loss. We present here a novel algorithmic approach that generates highly realistic digital models of human ret...

Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study.

Cardiovascular diabetology
BACKGROUND: Cardiac autonomic neuropathy (CAN) in diabetes mellitus (DM) is independently associated with cardiovascular (CV) events and CV death. Diagnosis of this complication of DM is time-consuming and not routinely performed in the clinical prac...

Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.

Journal of imaging informatics in medicine
Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in wh...