AIMC Topic: Deep Learning

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Genomic determinants of biological age estimated by deep learning applied to retinal images.

GeroScience
With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from latent information contained in retinal images. Retinal age gap (RAG) defined as the difference between c...

Deep Equilibrium Unfolding Learning for Noise Estimation and Removal in Optical Molecular Imaging.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe no...

PADS-Net: GAN-based radiomics using multi-task network of denoising and segmentation for ultrasonic diagnosis of Parkinson disease.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Parkinson disease (PD) is a prevalent neurodegenerative disorder, and its accurate diagnosis is crucial for timely intervention. We propose the PArkinson disease Denoising and Segmentation Network (PADS-Net), to simultaneously denoise and segment tra...

An efficient deep learning system for automatic detection of Acute Lymphoblastic Leukemia.

ISA transactions
Early and highly accurate detection of rapidly damaging deadly disease like Acute Lymphoblastic Leukemia (ALL) is essential for providing appropriate treatment to save valuable lives. Recent development in deep learning, particularly transfer learnin...

Brain tumour histopathology through the lens of deep learning: A systematic review.

Computers in biology and medicine
PROBLEM: Machine learning (ML)/Deep learning (DL) techniques have been evolving to solve more complex diseases, but it has been used relatively little in Glioblastoma (GBM) histopathological studies, which could benefit greatly due to the disease's c...

Deep learning model to identify and validate hypotension endotypes in surgical and critically ill patients.

British journal of anaesthesia
BACKGROUND: Hypotension is associated with organ injury and death in surgical and critically ill patients. In clinical practice, treating hypotension remains challenging because it can be caused by various underlying haemodynamic alterations. We aime...

Enhancing meteorological data reliability: An explainable deep learning method for anomaly detection.

Journal of environmental management
Accurate meteorological observation data is of great importance to human production activities. Meteorological observation systems have been advancing toward automation, intelligence, and informatization. Yet, instrumental malfunctions and unstable s...

Discrimination of Benign and Malignant Thyroid Nodules through Comparative Analyses of Human Saliva Samples via Metabolomics and Deep-Learning-Guided Label-free SERS.

ACS applied materials & interfaces
Thyroid nodules are a very common entity. The overall prevalence in the populace is estimated to be around 65-68%, among which a small portion (less than 5%) is malignant (cancerous). Therefore, it is important to discriminate benign thyroid nodules ...

Virtual Gram staining of label-free bacteria using dark-field microscopy and deep learning.

Science advances
Gram staining has been a frequently used staining protocol in microbiology. It is vulnerable to staining artifacts due to, e.g., operator errors and chemical variations. Here, we introduce virtual Gram staining of label-free bacteria using a trained ...

Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments.

Science advances
Pathogenic bioaerosols are critical for outbreaks of airborne disease; however, rapidly and accurately identifying pathogens directly from complex air environments remains highly challenging. We present an advanced method that combines open-set deep ...