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Deep Learning - AI Medical Compendium

AIMC Topic: Deep Learning

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Multi-compartment neuron and population encoding powered spiking neural network for deep distributional reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
Inspired by the brain's information processing using binary spikes, spiking neural networks (SNNs) offer significant reductions in energy consumption and are more adept at incorporating multi-scale biological characteristics. In SNNs, spiking neurons...

HCAR-AM ground nut leaf net: Hybrid convolution-based adaptive ResNet with attention mechanism for detecting ground nut leaf diseases with adaptive segmentation.

Network (Bristol, England)
Estimating the optimal answer is expensive for huge data resources that decrease the functionality of the system. To solve these issues, the latest groundnut leaf disorder identification model by deep learning techniques is implemented. The images ar...

Convolutional neural network for colorimetric glucose detection using a smartphone and novel multilayer polyvinyl film microfluidic device.

Scientific reports
Detecting glucose levels is crucial for diabetes patients as it enables timely and effective management, preventing complications and promoting overall health. In this endeavor, we have designed a novel, affordable point-of-care diagnostic device uti...

Generalizable Magnetic Resonance Imaging-based Nasopharyngeal Carcinoma Delineation: Bridging Gaps Across Multiple Centers and Raters With Active Learning.

International journal of radiation oncology, biology, physics
PURPOSE: To develop a deep learning method exploiting active learning and source-free domain adaptation for gross tumor volume delineation in nasopharyngeal carcinoma (NPC), addressing the variability and inaccuracy when deploying segmentation models...

Hessian-based mixed-precision quantization with transition aware training for neural networks.

Neural networks : the official journal of the International Neural Network Society
Model quantization is widely used to realize the promise of ubiquitous embedded deep network inference. While mixed-precision quantization has shown promising performance, existing approaches often rely on time-consuming search process to determine t...

Augmenting a spine CT scans dataset using VAEs, GANs, and transfer learning for improved detection of vertebral compression fractures.

Computers in biology and medicine
In recent years, deep learning has become a popular tool to analyze and classify medical images. However, challenges such as limited data availability, high labeling costs, and privacy concerns remain significant obstacles. As such, generative models...

Decoding face identity: A reverse-correlation approach using deep learning.

Cognition
Face recognition is crucial for social interactions. Traditional approaches primarily rely on subjective judgment, utilizing a pre-selected set of facial features based on literature or intuition to identify critical facial features for face recognit...

Validation of SynthSeg segmentation performance on CT using paired MRI from radiotherapy patients.

NeuroImage
INTRODUCTION: Manual segmentation of medical images is labor intensive and especially challenging for images with poor contrast or resolution. The presence of disease exacerbates this further, increasing the need for an automated solution. To this ex...

Identifying and quantifying multiple pollution sources in estuaries using fluorescence spectra and gradient-based deep learning.

Marine pollution bulletin
This study developed an intelligent method for identifying and quantifying water pollution sources in estuarine areas. It characterized the excitation-emission matrix (EEM) fluorescence spectra from seven end-members, including seawater, rainwater, a...

Deep learning pipeline for accelerating virtual screening in drug discovery.

Scientific reports
In the race to combat ever-evolving diseases, the drug discovery process often faces the hurdles of high-cost and time-consuming procedures. To tackle these challenges and enhance the efficiency of identifying new therapeutic agents, we introduce Vir...