AIMC Topic: Benchmarking

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The role of public challenges and data sets towards algorithm development, trust, and use in clinical practice.

Seminars in cutaneous medicine and surgery
In the past decade, machine learning and artificial intelligence have made significant advancements in pattern analysis, including speech and natural language processing, image recognition, object detection, facial recognition, and action categorizat...

Benchmarking machine learning methods for comprehensive chemical fingerprinting and pattern recognition.

Journal of chromatography. A
Machine learning (ML) has been used previously to recognize particular patterns of constituent compounds. Here, ML is used with comprehensive chemical fingerprints that capture the distribution of all constituent compounds to flexibly perform various...

Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified dermatologists. However, the non-availability of a public human benchmark restricts the comp...

Detecting and interpreting myocardial infarction using fully convolutional neural networks.

Physiological measurement
OBJECTIVE: We aim to provide an algorithm for the detection of myocardial infarction that operates directly on ECG data without any preprocessing and to investigate its decision criteria.

Use of Machine Learning to Identify Follow-Up Recommendations in Radiology Reports.

Journal of the American College of Radiology : JACR
PURPOSE: The aims of this study were to assess follow-up recommendations in radiology reports, develop and assess traditional machine learning (TML) and deep learning (DL) models in identifying follow-up, and benchmark them against a natural language...

Direct Feature Evaluation in Black-Box Optimization Using Problem Transformations.

Evolutionary computation
Exploratory Landscape Analysis provides sample-based methods to calculate features of black-box optimization problems in a quantitative and measurable way. Many problem features have been proposed in the literature in an attempt to provide insights i...

ADA-Tucker: Compressing deep neural networks via adaptive dimension adjustment tucker decomposition.

Neural networks : the official journal of the International Neural Network Society
Despite recent success of deep learning models in numerous applications, their widespread use on mobile devices is seriously impeded by storage and computational requirements. In this paper, we propose a novel network compression method called Adapti...

Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning.

Evolutionary computation
In this article, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models ...

KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides.

Journal of proteome research
Cell-penetrating peptides (CPPs) facilitate the transport of pharmacologically active molecules, such as plasmid DNA, short interfering RNA, nanoparticles, and small peptides. The accurate identification of new and unique CPPs is the initial step to ...

A hierarchical semi-supervised extreme learning machine method for EEG recognition.

Medical & biological engineering & computing
Feature extraction and classification is a vital part in motor imagery-based brain-computer interface (BCI) system. Traditional deep learning (DL) methods usually perform better with more labeled training samples. Unfortunately, the labeled samples a...