AIMC Topic: Benchmarking

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Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification.

Molecular & cellular proteomics : MCP Nov 1, 2021
Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human canc...
Phosphorylation Mice Humans Benchmarking Phosphopeptides Animals Proteomics Deep Learning Cell Line
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Hierarchical graph representations in digital pathology.

Medical image analysis Oct 27, 2021
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological ent...
Histological Techniques Neural Networks, Computer Prognosis Benchmarking Humans
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Deep Ensemble Learning Approaches in Healthcare to Enhance the Prediction and Diagnosing Performance: The Workflows, Deployments, and Surveys on the Statistical, Image-Based, and Sequential Datasets.

International journal of environmental research and public health Oct 14, 2021
With the development of information and technology, especially with the boom in big data, healthcare support systems are becoming much better. Patient data can be collected, retrieved, and stored in real time. These data are valuable and meaningful f...
Machine Learning Delivery of Health Care Reproducibility of Results Workflow Humans Benchmarking
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Self-supervised driven consistency training for annotation efficient histopathology image analysis.

Medical image analysis Oct 13, 2021
Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often expensive, laborious, and prone to inter and intra-observer variabilit...
Humans Neural Networks, Computer Image Processing, Computer-Assisted Benchmarking Supervised Machine Learning
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A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization.

JAMA network open Oct 1, 2021
IMPORTANCE: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic.
Adolescent Child, Preschool Child ROC Curve Middle Aged Infant Severity of Illness Index COVID-19 Testing Crowdsourcing Washington Clinical Decision Rules Models, Statistical Area Under Curve Benchmarking Infant, Newborn Hospitalization Aged Adult COVID-19 Male Humans Prognosis Algorithms Aged, 80 and over Machine Learning Female Young Adult
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Weakly Supervised Cell Segmentation by Point Annotation.

IEEE transactions on medical imaging Sep 30, 2021
We propose weakly supervised training schemes to train end-to-end cell segmentation networks that only require a single point annotation per cell as the training label and generate a high-quality segmentation mask close to those fully supervised meth...
Humans Neural Networks, Computer Supervised Machine Learning Image Processing, Computer-Assisted Benchmarking
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Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT.

NeuroImage Sep 25, 2021
Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more wide...
Brain Tomography, X-Ray Computed Female Deep Learning Magnetic Resonance Imaging Aged Male Humans Neural Networks, Computer Algorithms Benchmarking Image Interpretation, Computer-Assisted Gray Matter Neuroimaging White Matter Cerebral Cortex Birth Cohort
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Unsupervised cross-lingual model transfer for named entity recognition with contextualized word representations.

PloS one Sep 21, 2021
Named entity recognition (NER) is one fundamental task in the natural language processing (NLP) community. Supervised neural network models based on contextualized word representations can achieve highly-competitive performance, which requires a larg...
Multilingualism Algorithms Natural Language Processing Humans Linguistics Language Names Benchmarking Neural Networks, Computer Recognition, Psychology Semantics
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NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks.

Genome biology Sep 6, 2021
Long-read sequencing enables variant detection in genomic regions that are considered difficult-to-map by short-read sequencing. To fully exploit the benefits of longer reads, here we present a deep learning method NanoCaller, which detects SNPs usin...
Nanopore Sequencing INDEL Mutation Chromosome Mapping Major Histocompatibility Complex Haplotypes Base Sequence Genome, Human Humans Neural Networks, Computer Polymorphism, Single Nucleotide Alleles High-Throughput Nucleotide Sequencing Nanoparticles Benchmarking
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Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.

PloS one Sep 2, 2021
Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in h...
Fagopyrum Image Processing, Computer-Assisted Crops, Agricultural Crop Production Cluster Analysis Humans Helianthus Phenotype Benchmarking Deep Learning Markov Chains
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