AIMC Topic: Algorithms

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Usefulness of Data Simulation for Training Deep Learning Denoising Algorithms in Infrared Spectral Histology.

Analytical chemistry
This study investigates the use of simulated data to train deep learning models for denoising infrared spectral images of paraffin-embedded tissue sections in clinical applications. Noise in Fourier-transform infrared spectroscopy poses significant c...

Sensitive detection of structural dynamics using a statistical framework for comparative crystallography.

Science advances
Chemical and conformational changes are crucial to protein function and its pharmacological control. X-ray crystallography can reveal these changes in atomic detail, but standard analysis methods, which refine separate datasets, often overlook differ...

A smart community interactive art therapy platform based on multimodal computer graphics and resilient artificial intelligence for home-based elderly care.

Scientific reports
This research presents an innovative smart community interactive art therapy platform that integrates multimodal computer graphics with resilient artificial intelligence adaptation mechanisms to address the growing challenges of home-based elderly ca...

A multi-technique ensemble model leveraging attention mechanism and image processing for enhanced colorectal tumor detection.

Scientific reports
This research introduces an improved method for identifying colorectal tumors through a combination of deep convolutional neural networks (CNNs), transfer learning, and sophisticated image processing techniques used on histopathological images. The s...

Hybrid radiomic-HOG ensemble model for accurate pulmonary nodule diagnosis.

Biomedical physics & engineering express
Lung cancer remains one of the deadliest forms of cancer worldwide, making early and accurate pulmonary-nodule classification essential for improving patient prognosis. This study presents a robust ensemble-stacking framework that integrates Histogra...

Towards real-time non-invasive detection of hyperlipidemia through finger pulse image analysis using deep learning.

Biomedical physics & engineering express
Hyperlipidemia detection involves invasive, time-consuming procedures requiring clinical laboratories and blood samples. Often asymptomatic in its early stages, hyperlipidemia significantly increases the risk of cardiovascular diseases. The objective...

Graph-based deep reinforcement learning for haplotype assembly with Ralphi.

Genome research
Haplotype assembly is the problem of reconstructing the combination of alleles on the maternally and paternally inherited chromosome copies. Individual haplotypes are essential to our understanding of how combinations of different variants impact phe...

From root to result: Portable NIRS-based non-destructive prediction of cassava quality traits.

PloS one
Cassava (Manihot esculenta Crantz) is a staple food and a key industrial crop across tropical regions, but traditional phenotyping for critical quality traits like dry matter content (DMC) and starch content (StC) is a laborious and low-throughput pr...

PixlMap: A generalisable pixel classifier for cellular phenotyping in multiplex immunofluorescence images.

PloS one
Multiplexed methods for the detection of protein expression generate extremely data-rich images of intact tissue sections. These images are invaluable for the quantification and analysis of complex biology and biomarker development. However, their in...

Single replica spin-glass phase detection using field variation and machine learning.

PloS one
The Sherrington-Kirkpatrick (SK) spin-glass model exhibits well-studied phase transitions that are mostly established using replica-based methods. Regardless of the method used for detection, the intrinsic phase of a system exists whether or not repl...