AIMC Topic: Deep Learning

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Differentiation of optic disc edema and pseudopapilledema with deep learning on near-infrared reflectance images.

BMC ophthalmology
BACKGROUND: This study aimed to develop an artificial intelligence-based deep learning (DL) algorithm using near-infrared reflectance (NIR) images to differentiate between optic disc edema and pseudopapilledema, and to evaluate the diagnostic perform...

A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives.

BMC cancer
PURPOSE: Breast cancer remains a leading cause of mortality in women worldwide, with notable disparities in incidence and prognosis across regions. This systematic review explores the application of Deep Learning-based computer-aided diagnostic (CAD)...

Deep multi-instance learning model based on gadoxetic acid-enhanced MRI for predicting microvascular invasion of hepatocellular carcinoma: a multicenter, retrospective study.

BMC cancer
OBJECTIVE: Microvascular invasion (MVI) is of great significance for the individualized treatment of hepatocellular carcinoma (HCC) and preoperative noninvasive prediction of MVI is still an urgent clinical problem. To explore the effects of differen...

Efficient deep neural networks for cancer detection on histopathology combining attention and image downsampling.

Scientific reports
Pathology diagnosis of colorectal cancer is time-consuming and requires a high level of expertise. However, it is an essential step towards establishing the adequate treatment. The need to analyse a large number of these histopathological images call...

Fine-tuned ResNet34 for efficient brain tumor classification.

Scientific reports
Brain tumors are among the most fatal diseases, Often leading to a reduction in life expectancy. Early and accurate diagnosis is essential to guide effective treatment and enhance survival rates. Advances in artificial intelligence, particularly deep...

GraphComm predicts cell cell communication using a graph based deep learning method in single cell RNA sequencing data.

Scientific reports
Interactions between cells coordinate various functions across cell-types in health and disease states. Novel single-cell techniques enable deep investigation of cellular crosstalk at single-cell resolution. Cell-cell communication (CCC) is mediated ...

Multi-objective optimization of electromagnetic vibration parameters for corn seed phenotype prediction based on deep learning.

Scientific reports
This study presents a novel framework for adaptive optimization of electromagnetic vibration parameters in corn seed treatment using multi-objective deep learning approaches. A hybrid CNN-LSTM network architecture was developed to process heterogeneo...

Visual feature-based multi-scale hybrid attention network for fine-grained Hawthorn varieties identification.

Scientific reports
Hawthorn is a well-known economic crop widely recognized for its efficacy in cardiovascular protection and blood pressure reduction. However, accurately identifying Hawthorn varieties, which arise from diverse cultivation conditions, poses a signific...

Image complexity-based fMRI-BOLD visual network categorization across visual datasets using topological descriptors and deep-hybrid learning.

Scientific reports
This study proposes a new approach that investigates differences in topological characteristics of visual networks, which are constructed using fMRI BOLD time-series corresponding to visual datasets of COCO, ImageNet, and SUN. A publicly available BO...