AIMC Topic: Algorithms

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Low-cost algorithms for clinical notes phenotype classification to enhance epidemiological surveillance: A case study.

Journal of biomedical informatics
OBJECTIVE: Our study aims to enhance epidemic intelligence through event-based surveillance in an emerging pandemic context. We classified electronic health records (EHRs) from La Rioja, Argentina, focusing on predicting COVID-19-related categories i...

Mathematical and numerical tumour development modelling for personalised treatment planning.

Biomechanics and modeling in mechanobiology
This paper presents a mathematical and numerical framework for modelling and parametrising tumour evolution dynamics to enhance computer-aided diagnosis and personalised treatment. The model comprises six differential equations describing cancer cell...

A Causality-Aware Paradigm for Evaluating Creativity of Multimodal Large Language Models.

IEEE transactions on pattern analysis and machine intelligence
Recently, numerous benchmarks have been developed to evaluate the logical reasoning abilities of large language models (LLMs). However, assessing the equally important creative capabilities of LLMs is challenging due to the subjective, diverse, and d...

Aligning, Autoencoding and Prompting Large Language Models for Novel Disease Reporting.

IEEE transactions on pattern analysis and machine intelligence
Given radiology images, automatic radiology report generation aims to produce informative text that reports diseases. It can benefit current clinical practice in diagnostic radiology. Existing methods typically rely on large-scale medical datasets an...

Gas Sensor Drift Compensation Using Semi-Supervised Ensemble Classifiers with Multi-Level Features and Center Loss.

ACS sensors
The drift compensation of gas sensors is a significant and challenging issue in the field of electronic noses (E-nose). Compensating sensor drift has a great benefit in improving the performance of E-nose systems. However, conventional methods often ...

NeuroNasal: Advanced AI-Driven Self-Supervised Learning Approach for Enhanced Sinonasal Pathology Detection.

Sensors (Basel, Switzerland)
Sinus diseases are inflammations or infections of the sinuses that significantly impact patient quality of life. They cause nasal congestion, facial pain, headaches, thick nasal discharge, and a reduced sense of smell. However, accurately diagnosing ...

Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges.

Sensors (Basel, Switzerland)
The intricate imaging structures, artifacts, and noise present in ultrasound images and videos pose significant challenges for accurate segmentation. Deep learning has recently emerged as a prominent field, playing a crucial role in medical image pro...

Application of machine learning in assessing disease activity in SLE.

Lupus science & medicine
OBJECTIVE: SLE is a chronic autoimmune disease with immune complex deposition in various organs, causing inflammation. The Systemic Lupus Erythematosus Disease Activity Index 2000 assesses disease severity but is subjective. This study aimed to const...

Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study.

BMC oral health
BACKGROUND: Manual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft an...

Multi-scale conv-attention U-Net for medical image segmentation.

Scientific reports
U-Net-based network structures are widely used in medical image segmentation. However, effectively capturing multi-scale features and spatial context information of complex organizational structures remains a challenge. To address this, we propose a ...