AIMC Topic: Benchmarking

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Characteristics and Emerging Trends in Research on Rehabilitation Robots from 2001 to 2020: Bibliometric Study.

Journal of medical Internet research
BACKGROUND: The past 2 decades have seen rapid development in the use of robots for rehabilitation. Research on rehabilitation robots involves interdisciplinary activities, making it a great challenge to obtain comprehensive insights in this research...

Comparison of evaluation metrics of deep learning for imbalanced imaging data in osteoarthritis studies.

Osteoarthritis and cartilage
PURPOSE: To compare the evaluation metrics for deep learning methods that were developed using imbalanced imaging data in osteoarthritis studies.

An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization.

Scientific data
Medical Assisted Reproduction proved its efficacy to treat the vast majority forms of infertility. One of the key procedures in this treatment is the selection and transfer of the embryo with the highest developmental potential. To assess this potent...

Show Your Work: Responsible Model Reporting in Health Care Artificial Intelligence.

The Surgical clinics of North America
Standardized and thorough model reporting is an integral component in the development and deployment of machine learning models in health care. Model reporting includes sharing multiple model performance metrics and incorporating metadata to provide ...

JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments.

IEEE transactions on pattern analysis and machine intelligence
We present JRDB, a novel egocentric dataset collected from our social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360 RGB video at 15 fps, 3D point clouds from two 1...

Establishment and Evaluation of Intelligent Diagnostic Model for Ophthalmic Ultrasound Images Based on Deep Learning.

Ultrasound in medicine & biology
OBJECTIVE: The goal of the work described here was to construct a deep learning-based intelligent diagnostic model for ophthalmic ultrasound images to provide auxiliary analysis for the intelligent clinical diagnosis of posterior ocular segment disea...

Genomic benchmarks: a collection of datasets for genomic sequence classification.

BMC genomic data
BACKGROUND: Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental met...

An enhanced Runge Kutta boosted machine learning framework for medical diagnosis.

Computers in biology and medicine
With the development and maturity of machine learning methods, medical diagnosis aided with machine learning methods has become a popular method to assist doctors in diagnosing and treating patients. However, machine learning methods are greatly affe...

Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.

NeuroImage
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding ...

Relating process and outcome metrics for meaningful and interpretable cannulation skill assessment: A machine learning paradigm.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: The quality of healthcare delivery depends directly on the skills of clinicians. For patients on hemodialysis, medical errors or injuries caused during cannulation can lead to adverse outcomes, including potential death. To...