AIMC Topic: Deep Learning

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Isolation and reconstruction of cardiac mitochondria from SBEM images using a deep learning-based method.

Journal of structural biology
Mitochondrial morphological defects are a common feature of diseased cardiac myocytes. However, quantitative assessment of mitochondrial morphology is limited by the time-consuming manual segmentation of electron micrograph (EM) images. To advance un...

Integrating mechanistic and deep learning models for accurately predicting the enrichment of polyhydroxyalkanoates accumulating bacteria in mixed microbial cultures.

Bioresource technology
The enrichment of polyhydroxyalkanoates (PHA) accumulating bacteria (PAB) in mixed microbial cultures (MMC) is extremely difficult to be predicted and optimized. Here we demonstrate that mechanistic and deep learning models can be integrated innovati...

Multi-Run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers.

International journal of molecular sciences
BACKGROUND: Long non-coding RNA plays a vital role in changing the expression profiles of various target genes that lead to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy.

pValid 2: A deep learning based validation method for peptide identification in shotgun proteomics with increased discriminating power.

Journal of proteomics
Tandem mass spectrometry has been the principal method in shotgun proteomics for peptide and protein identification. However, incorrect identifications reported by proteome search engines are still unknown, and further validation methods are needed. ...

Prediction of corneal back surface power - Deep learning algorithm versus multivariate regression.

Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)
BACKGROUND: The corneal back surface is known to add some against the rule astigmatism, with implications in cataract surgery with toric lens implantation. This study aimed to set up and validate a deep learning algorithm to predict corneal back surf...

Data Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection.

Sensors (Basel, Switzerland)
Custom inspection using X-ray imaging is a very promising application of modern pattern recognition technology. However, the lack of data or renewal of tariff items makes the application of such technology difficult. In this paper, we present a data ...

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion.

Sensors (Basel, Switzerland)
In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of ...

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images.

Nature communications
Machine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole...

Deep learning for osteoarthritis classification in temporomandibular joint.

Oral diseases
OBJECTIVES: This study aimed to develop a diagnostic support tool using pretrained models for classifying panoramic images of the temporomandibular joint (TMJ) into normal and osteoarthritis (OA) cases.