AIMC Topic: Benchmarking
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Establishment and Evaluation of Intelligent Diagnostic Model for Ophthalmic Ultrasound Images Based on Deep Learning.
Ultrasound in medicine & biology
May 1, 2023
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
May 1, 2023
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
Apr 25, 2023
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
Apr 20, 2023
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
Apr 18, 2023
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...
Rema-Net: An efficient multi-attention convolutional neural network for rapid skin lesion segmentation.
Computers in biology and medicine
Apr 17, 2023
For clinical treatment, the accurate segmentation of lesions from dermoscopic images is extremely valuable. Convolutional neural networks (such as U-Net and its numerous variants) have become the main methods for skin lesion segmentation in recent ye...
Benchmarking explanation methods for mental state decoding with deep learning models.
NeuroImage
Apr 13, 2023
Deep learning (DL) models find increasing application in mental state decoding, where researchers seek to understand the mapping between mental states (e.g., experiencing anger or joy) and brain activity by identifying those spatial and temporal feat...
Clinical metrics and tools for provider assessment and tracking of trigonocephaly.
Journal of neurosurgery. Pediatrics
Apr 7, 2023
OBJECTIVE: Quantitative measurements of trigonocephaly can be used to characterize and track this phenotype, which is associated with metopic craniosynostosis. Traditionally, trigonocephaly metrics were extracted from CT scans; however, this method e...
Trustworthy Deep Neural Network for Inferring Anticancer Synergistic Combinations.
IEEE journal of biomedical and health informatics
Apr 4, 2023
The lack of a gold standard synergy quantification method for chemotherapeutic drug combinations warrants the consideration of different synergy metrics to develop efficient predictive models. Furthermore, neglecting combination sensitivity may lead ...
Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples.
IEEE/ACM transactions on computational biology and bioinformatics
Apr 3, 2023
Many high-performance DTA deep learning models have been proposed, but they are mostly black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA models more trustworthy, and allows to distill biological knowledge from the mode...
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