AIMC Topic: Deep Learning

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TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework.

PloS one
Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variatio...

Utility of Thin-slice Fat-suppressed Single-shot T2-weighted MR Imaging with Deep Learning Image Reconstruction as a Protocol for Evaluating the Pancreas.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: To compare the utility of thin-slice fat-suppressed single-shot T2-weighted imaging (T2WI) with deep learning image reconstruction (DLIR) and conventional fast spin-echo T2WI with DLIR for evaluating pancreatic protocol.

A deep learning approach to detection of oral cancer lesions from intra oral patient images: A preliminary retrospective study.

Journal of stomatology, oral and maxillofacial surgery
INTRODUCTION: Oral squamous cell carcinomas (OSCC) seen in the oral cavity are a category of diseases for which dentists may diagnose and even cure. This study evaluated the performance of diagnostic computer software developed to detect oral cancer ...

Deep learning-based IDH1 gene mutation prediction using histopathological imaging and clinical data.

Computers in biology and medicine
In the field of histopathology, many studies on the classification of whole slide images (WSIs) using artificial intelligence (AI) technology have been reported. We have studied the disease progression assessment of glioma. Adult-type diffuse gliomas...

δ-Conotoxin Structure Prediction and Analysis through Large-Scale Comparative and Deep Learning Modeling Approaches.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The δ-conotoxins, a class of peptides produced in the venom of cone snails, are of interest due to their ability to inhibit the inactivation of voltage-gated sodium channels causing paralysis and other neurological responses, but difficulties in thei...

A systematic review of deep learning-based spinal bone lesion detection in medical images.

Acta radiologica (Stockholm, Sweden : 1987)
Spinal bone lesions encompass a wide array of pathologies, spanning from benign abnormalities to aggressive malignancies, such as diffusely localized metastases. Early detection and accurate differentiation of the underlying diseases is crucial for e...

Deep representation learning from electronic medical records identifies distinct symptom based subtypes and progression patterns for COVID-19 prognosis.

International journal of medical informatics
OBJECTIVE: Symptoms are significant kind of phenotypes for managing and controlling of the burst of acute infectious diseases, such as COVID-19. Although patterns of symptom clusters and time series have been considered the high potential prediction ...

Application and performance enhancement of FAIMS spectral data for deep learning analysis using generative adversarial network reinforcement.

Analytical biochemistry
When using High-field asymmetric ion mobility spectrometry (FAIMS) to process complex mixtures for deep learning analysis, there is a problem of poor recognition performance due to the lack of high-quality data and low sample diversity. In this paper...

An XAI-enhanced efficientNetB0 framework for precision brain tumor detection in MRI imaging.

Journal of neuroscience methods
BACKGROUND: Accurately diagnosing brain tumors from MRI scans is crucial for effective treatment planning. While traditional methods heavily rely on radiologist expertise, the integration of AI, particularly Convolutional Neural Networks (CNNs), has ...