AIMC Topic: Benchmarking

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KDE-GAN: A multimodal medical image-fusion model based on knowledge distillation and explainable AI modules.

Computers in biology and medicine Nov 3, 2022
BACKGROUND: As medical images contain sensitive patient information, finding a publicly accessible dataset with patient permission is challenging. Furthermore, few large-scale datasets suitable for training image-fusion models are available. To addre...
Humans Benchmarking Entropy Artificial Intelligence Image Processing, Computer-Assisted
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Benchmarking of Deep Architectures for Segmentation of Medical Images.

IEEE transactions on medical imaging Oct 27, 2022
In recent years, there were many suggestions regarding modifications of the well-known U-Net architecture in order to improve its performance. The central motivation of this work is to provide a fair comparison of U-Net and its five extensions using ...
Tomography, X-Ray Computed Benchmarking Deep Learning Magnetic Resonance Imaging Image Processing, Computer-Assisted
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Explainable multi-module semantic guided attention based network for medical image segmentation.

Computers in biology and medicine Oct 25, 2022
Automated segmentation of medical images is crucial for disease diagnosis and treatment planning. Medical image segmentation has been improved based on the convolutional neural networks (CNNs) models. Unfortunately, they are still limited by scenario...
Neuroimaging Benchmarking Semantics Image Processing, Computer-Assisted Neural Networks, Computer
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The robot doesn't lie: real-life validation of robotic performance metrics.

Surgical endoscopy Oct 20, 2022
BACKGROUND: Degree of resident participation in a case is often used as a surrogate marker for operative autonomy, an essential element of surgical resident training. Previous studies have demonstrated a considerable disagreement between the percepti...
Benchmarking Internship and Residency Robotic Surgical Procedures Hernia, Inguinal Humans Clinical Competence Robotics
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E-DU: Deep neural network for multimodal medical image segmentation based on semantic gap compensation.

Computers in biology and medicine Oct 12, 2022
BACKGROUND: U-Net includes encoder, decoder and skip connection structures. It has become the benchmark network in medical image segmentation. However, the direct fusion of low-level and high-level convolution features with semantic gaps by tradition...
Neural Networks, Computer Benchmarking Semantics
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MRBENet: A Multiresolution Boundary Enhancement Network for Salient Object Detection.

Computational intelligence and neuroscience Oct 10, 2022
Salient Object Detection (SOD) simulates the human visual perception in locating the most attractive objects in the images. Existing methods based on convolutional neural networks have proven to be highly effective for SOD. However, in some cases, th...
Attention Visual Perception Neural Networks, Computer Semantics Humans Benchmarking
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What are clinically relevant performance metrics in robotic surgery? A systematic review of the literature.

Journal of robotic surgery Oct 3, 2022
A crucial element of any surgical training program is the ability to provide procedure-specific, objective, and reliable measures of performance. During robotic surgery, objective clinically relevant performance metrics (CRPMs) can provide tailored c...
Computer Simulation Clinical Competence Humans Curriculum Robotic Surgical Procedures Robotics Benchmarking
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MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images.

IEEE transactions on medical imaging Sep 30, 2022
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they oft...
Humans Algorithms Benchmarking Machine Learning
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Editorial Comment: Are Body Composition Metrics Applicable to Clinical Practice?

AJR. American journal of roentgenology Sep 14, 2022
Body Composition Benchmarking Outpatients Cardiovascular Diseases Deep Learning Tomography, X-Ray Computed Humans
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Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications.

Artificial intelligence in medicine Aug 27, 2022
BACKGROUND: The application of artificial intelligence to interpret the electrocardiogram (ECG) has predominantly included the use of knowledge engineered rule-based algorithms which have become widely used today in clinical practice. However, over r...
Machine Learning Algorithms Artificial Intelligence Benchmarking Time Factors Electrocardiography Humans
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