Primary Care

Smoking & Tobacco

Latest AI and machine learning research in smoking & tobacco for healthcare professionals.

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Robotic Fast Patch Clamp in Brain Slices Based on Stepwise Micropipette Navigation and Gigaseal Formation Control.

The patch clamp technique has become the gold standard for neuron electrophysiology research in brai...

ChatExosome: An Artificial Intelligence (AI) Agent Based on Deep Learning of Exosomes Spectroscopy for Hepatocellular Carcinoma (HCC) Diagnosis.

Large language models (LLMs) hold significant promise in the field of medical diagnosis. There are s...

A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus.

Quantifying emesis in Suncus murinus (S. murinus) has traditionally relied on direct observation or ...

Explainable AI-driven scalogram analysis and optimized transfer learning for sleep apnea detection with single-lead electrocardiograms.

Sleep apnea, a fatal sleep disorder causing repetitive respiratory cessation, requires immediate int...

Residual networks using multi-task learning algorithm for near-infrared spectroscopy: A case study.

Near-infrared spectroscopy (NIRS) is a widely used non-destructive detection method known for its ef...

Weakly supervised multi-modal contrastive learning framework for predicting the HER2 scores in breast cancer.

Human epidermal growth factor receptor 2 (HER2) is an important biomarker for prognosis and predicti...

Retrieval of nicotine content in cigar leaves by remote analysis of aerial hyperspectral combining machine learning methods.

Cigar leaf is a special type of tobacco plant, which is the raw material for producing high-quality ...

A deep learning model for QRS delineation in organized rhythms during in-hospital cardiac arrest.

BACKGROUND: Cardiac arrest (CA) is the sudden cessation of heart function, typically resulting in lo...

SeLa-MIL: Developing an instance-level classifier via weakly-supervised self-training for whole slide image classification.

BACKGROUND AND OBJECTIVE: Pathology image classification is crucial in clinical cancer diagnosis and...

Enhancing waste classification accuracy with Channel and Spatial Attention-Based Multiblock Convolutional Network.

Municipal waste classification is significant for effective recycling and waste management processes...

Dynamic graph based weakly supervised deep hashing for whole slide image classification and retrieval.

Recently, a multi-scale representation attention based deep multiple instance learning method has pr...

PEDRA-EFB0: colorectal cancer prognostication using deep learning with patch embeddings and dual residual attention.

In computer-aided diagnosis systems, precise feature extraction from CT scans of colorectal cancer u...

Robotic Fast Dual-Arm Patch Clamp System for Mechanosensitive Excitability Research of Neurons.

OBJECTIVE: A robotic fast dual-arm patch clamp system with controllable mechanical stimulation is pr...

S2P-Matching: Self-Supervised Patch-Based Matching Using Transformer for Capsule Endoscopic Images Stitching.

The Magnetically Controlled Capsule Endoscopy (MCCE) has a limited shooting range, resulting in capt...

Predicting doxorubicin-induced cardiotoxicity in breast cancer: leveraging machine learning with synthetic data.

Doxorubicin (DOXO) is a primary treatment for breast cancer but can cause cardiotoxicity in over 25%...

Immunological composition of human milk before and during subclinical and clinical mastitis.

Mastitis, an inflammatory condition affecting more than 25% of breastfeeding women, is usually assoc...

Patch-Wise Deep Learning Method for Intracranial Stenosis and Aneurysm Detection-the Tromsø Study.

Intracranial atherosclerotic stenosis (ICAS) and intracranial aneurysms are prevalent conditions in ...

When multiple instance learning meets foundation models: Advancing histological whole slide image analysis.

Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised learning method...

Toward efficient slide-level grading of liver biopsy via explainable deep learning framework.

In the context of chronic liver diseases, where variability in progression necessitates early and pr...

The KMeansGraphMIL Model: A Weakly Supervised Multiple Instance Learning Model for Predicting Colorectal Cancer Tumor Mutational Burden.

Colorectal cancer (CRC) is one of the top three most lethal malignancies worldwide, posing a signifi...

Patch-based feature mapping with generative adversarial networks for auxiliary hip fracture detection.

BACKGROUND: Hip fractures are a significant public health issue, particularly among the elderly popu...

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