AIMC Topic: Deep Learning

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Deep learning in random neural fields: Numerical experiments via neural tangent kernel.

Neural networks : the official journal of the International Neural Network Society
A biological neural network in the cortex forms a neural field. Neurons in the field have their own receptive fields, and connection weights between two neurons are random but highly correlated when they are in close proximity in receptive fields. In...

Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics.

Medical & biological engineering & computing
Dynamic contrast-enhanced MRI (DCE-MRI) is routinely included in the prostate MRI protocol for a long time; its role has been questioned. It provides rich spatial and temporal information. However, the contained information cannot be fully extracted ...

Prediction of postoperative infection in elderly using deep learning-based analysis: an observational cohort study.

Aging clinical and experimental research
Elderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly...

A deep learning model to identify the fluid overload status in critically ill patients based on chest X-ray images.

Polish archives of internal medicine
INTRODUCTION: Recent studies have highlighted adverse outcomes of fluid overload in critically ill patients. Therefore, its early recognition is essential for the management of these patients.

Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: A neuro-oncological investigation.

Computers in biology and medicine
BACKGROUND: The O6-methylguanine-DNA methyltransferase (MGMT) is a deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential clinical brain tumor biomarker for Glioblastoma Multiforme (GBM). Knowing the status of MGMT met...

Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches.

Cells
Induced pluripotent stem cells (iPSCs) can be differentiated into mesenchymal stem cells (iPSC-MSCs), retinal ganglion cells (iPSC-RGCs), and retinal pigmental epithelium cells (iPSC-RPEs) to meet the demand of regeneration medicine. Since the produc...

Tubule-U-Net: a novel dataset and deep learning-based tubule segmentation framework in whole slide images of breast cancer.

Scientific reports
The tubule index is a vital prognostic measure in breast cancer tumor grading and is visually evaluated by pathologists. In this paper, a computer-aided patch-based deep learning tubule segmentation framework, named Tubule-U-Net, is developed and pro...

Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning.

Nature communications
Biopsy is the recommended standard for pathological diagnosis of liver carcinoma. However, this method usually requires sectioning and staining, and well-trained pathologists to interpret tissue images. Here, we utilize Raman spectroscopy to study hu...

A Benchmark Dataset of Endoscopic Images and Novel Deep Learning Method to Detect Intestinal Metaplasia and Gastritis Atrophy.

IEEE journal of biomedical and health informatics
Endoscopy has been routinely used to diagnose stomach diseases including intestinal metaplasia (IM) and gastritis atrophy (GA). Such routine examination usually demands highly skilled radiologists to focus on a single patient with substantial time, c...

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning.

IEEE journal of biomedical and health informatics
Preliminary microbiological diagnosis usually relies on microscopic examination and, due to the routine culture and bacteriological examination, lasts up to 11 days. Hence, many deep learning methods based on microscopic images were recently introduc...