AIMC Topic: Benchmarking

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Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.

Nucleic acids research
Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics disco...

Benchmarking the speed-accuracy tradeoff in object recognition by humans and neural networks.

Journal of vision
Active object recognition, fundamental to tasks like reading and driving, relies on the ability to make time-sensitive decisions. People exhibit a flexible tradeoff between speed and accuracy, a crucial human skill. However, current computational mod...

Benchmarking Automatic Speech Recognition Technology for Natural Language Samples of Children With and Without Developmental Delays.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Natural language sampling (NLS) offers rich insights into real-world speech and language usage across diverse groups; yet, human transcription is time-consuming and costly. Automatic speech recognition (ASR) technology has the potential to revolution...

A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.

Briefings in bioinformatics
A major challenge of precision oncology is the identification and prioritization of suitable treatment options based on molecular biomarkers of the considered tumor. In pursuit of this goal, large cancer cell line panels have successfully been studie...

Semi-supervised Learning for Generalizable Intracranial Hemorrhage Detection and Segmentation.

Radiology. Artificial intelligence
Purpose To develop and evaluate a semi-supervised learning model for intracranial hemorrhage detection and segmentation on an out-of-distribution head CT evaluation set. Materials and Methods This retrospective study used semi-supervised learning to ...

MA-PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism.

Protein science : a publication of the Protein Society
AntiCancer Peptides (ACPs) have emerged as promising therapeutic agents for cancer treatment. The time-consuming and costly nature of wet-lab discriminatory methods has spurred the development of various machine learning and deep learning-based ACP c...

Unveiling new patterns: A surgical deep learning model for intestinal obstruction management.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Swift and accurate decision-making is pivotal in managing intestinal obstructions. This study aims to integrate deep learning and surgical expertise to enhance decision-making in intestinal obstruction cases.

Deep learning for automated segmentation in radiotherapy: a narrative review.

The British journal of radiology
The segmentation of organs and structures is a critical component of radiation therapy planning, with manual segmentation being a laborious and time-consuming task. Interobserver variability can also impact the outcomes of radiation therapy. Deep neu...