AIMC Topic: Deep Learning

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Validation of a fingertip home sleep apnea testing system using deep learning AI and a temporal event localization analysis.

Sleep
STUDY OBJECTIVES: This paper validates TipTraQ, a compact home sleep apnea testing (HSAT) system. TipTraQ comprises a fingertip-worn device, a mobile application, and a cloud-based deep learning artificial intelligence (AI) system. The device utilize...

Using Masked Image Modelling Transformer Architecture for Laparoscopic Surgical Tool Classification and Localization.

Sensors (Basel, Switzerland)
Artificial intelligence (AI) has shown its potential to advance applications in various medical fields. One such area involves developing integrated AI-based systems to assist in laparoscopic surgery. Surgical tool detection and phase recognition are...

A novel framework for esophageal cancer grading: combining CT imaging, radiomics, reproducibility, and deep learning insights.

BMC gastroenterology
OBJECTIVE: This study aims to create a reliable framework for grading esophageal cancer. The framework combines feature extraction, deep learning with attention mechanisms, and radiomics to ensure accuracy, interpretability, and practical use in tumo...

A deep learning framework for virtual continuous glucose monitoring and glucose prediction based on life-log data.

Scientific reports
While continuous glucose monitoring (CGM) has revolutionized metabolic health management, widespread adoption remains limited by cost constraints and usage burden, often resulting in interrupted monitoring periods. We propose a deep learning framewor...

Pose estimation and tracking dataset for multi-animal behavior analysis on the China Space Station.

Scientific data
Non-contact behavioral study through intelligent image analysis is becoming increasingly vital in animal neuroscience and ethology. The shift from traditional "black box" methods to more open and intelligent approaches is driven by advances in deep l...

ReQuant: improved base modification calling by k-mer value imputation.

Nucleic acids research
Nanopore sequencing allows identification of base modifications, such as methylation, directly from raw current data. Prevailing approaches, including deep learning (DL) methods, require training data covering all possible sequence contexts. These da...

Reinforcement learning for automated method development in liquid chromatography: insights in the reward scheme and experimental budget selection.

Journal of chromatography. A
Chromatographic problem solving, commonly referred to as method development (MD), is hugely complex, given the many operational parameters that must be optimized and their large effect on the elution times of individual sample compounds. Recently, th...

Enhanced Prognostication of Early Breast Cancer Outcomes Using Deep Learning on Merged Multistain and Multicolor-Depth Tumor Histopathology.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
Accurate breast cancer prognosis helps clinicians in selecting optimal treatments, potentially improving patient survival. We tested whether combining deep learning with tumor histopathology images could reliably predict cancer spread. Advantages of ...

Three-Dimensional Reconstruction of Serial Block-Face Scanning Electron Microscopy Using Semantic Segmentation based on Semi-Supervised Deep Learning.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
Serial block-face scanning electron microscopy (SBF-SEM) is employed to achieve high-resolution volume reconstructions and detailed ultrastructural analyses of complex organelles. The performance of SBF-SEM is evaluated according to the accuracy of s...

SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity.

BMC biology
BACKGROUND: Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research. Affinity prediction based on deep learning methods has proven crucial to ...