AI Medical Compendium Topic

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FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation.

IEEE transactions on neural networks and learning systems
The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has es...

Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review.

Journal of biomedical informatics
INTRODUCTION: The use of artificial intelligence (AI), particularly machine learning and predictive analytics, has shown great promise in health care. Despite its strong potential, there has been limited use in health care settings. In this systemati...

DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Artificial intelligence (AI) has several uses in the healthcare industry, some of which include healthcare management, medical forecasting, practical making of decisions, and diagnosis. AI technologies have reached human-lik...

Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial.

Journal of medical Internet research
Deep learning-based clinical imaging analysis underlies diagnostic artificial intelligence (AI) models, which can match or even exceed the performance of clinical experts, having the potential to revolutionize clinical practice. A wide variety of aut...

MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis.

IEEE journal of biomedical and health informatics
One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated cer...

Detecting changes in the performance of a clinical machine learning tool over time.

EBioMedicine
BACKGROUND: Excessive use of blood cultures (BCs) in Emergency Departments (EDs) results in low yields and high contamination rates, associated with increased antibiotic use and unnecessary diagnostics. Our team previously developed and validated a m...

Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis.

Sensors (Basel, Switzerland)
Computer vision and deep learning have the potential to improve medical artificial intelligence (AI) by assisting in diagnosis, prediction, and prognosis. However, the application of deep learning to medical image analysis is challenging due to limit...

Expanding the utility of robotics for pancreaticoduodenectomy: a 10-year review and comparison to international benchmarks in pancreatic surgery.

Surgical endoscopy
BACKGROUND: Robotic pancreaticoduodenectomy (RPD) is an emerging alternative to open pancreaticoduodenectomy (OPD). Although RPD offers various theoretical advantages, it is used in less than 10% of all pancreaticoduodenectomies. The aim of this stud...

Understanding calibration of deep neural networks for medical image classification.

Computer methods and programs in biomedicine
Background and Objective - In the field of medical image analysis, achieving high accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence scores of a deep neural network play a pivotal role in explainability by provid...

SelANet: decision-assisting selective sleep apnea detection based on confidence score.

BMC medical informatics and decision making
BACKGROUND: One of the most common sleep disorders is sleep apnea syndrome. To diagnose sleep apnea syndrome, polysomnography is typically used, but it has limitations in terms of labor, cost, and time. Therefore, studies have been conducted to devel...