AIMC Topic: Benchmarking
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Unveiling new patterns: A surgical deep learning model for intestinal obstruction management.
The international journal of medical robotics + computer assisted surgery : MRCAS
Feb 1, 2024
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
Jan 23, 2024
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...
Benchmarking the most popular XAI used for explaining clinical predictive models: Untrustworthy but could be useful.
Health informatics journal
Jan 1, 2024
OBJECTIVE: This study aimed to assess the practicality and trustworthiness of explainable artificial intelligence (XAI) methods used for explaining clinical predictive models.
Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications.
Radiology
Jan 1, 2024
Although chatbots have existed for decades, the emergence of transformer-based large language models (LLMs) has captivated the world through the most recent wave of artificial intelligence chatbots, including ChatGPT. Transformers are a type of neura...
Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.
Radiology. Artificial intelligence
Jan 1, 2024
Purpose To present results from a literature survey on practices in deep learning segmentation algorithm evaluation and perform a study on expert quality perception of brain tumor segmentation. Materials and Methods A total of 180 articles reporting ...
MESPool: Molecular Edge Shrinkage Pooling for hierarchical molecular representation learning and property prediction.
Briefings in bioinformatics
Nov 22, 2023
Identifying task-relevant structures is important for molecular property prediction. In a graph neural network (GNN), graph pooling can group nodes and hierarchically represent the molecular graph. However, previous pooling methods either drop out no...
Machine learning models to predict surgical case duration compared to current industry standards: scoping review.
BJS open
Nov 1, 2023
BACKGROUND: Surgical waiting lists have risen dramatically across the UK as a result of the COVID-19 pandemic. The effective use of operating theatres by optimal scheduling could help mitigate this, but this requires accurate case duration prediction...
Total Force Fitness Standardization: Leveraging Policy and Metrics to Inform and Accelerate Implementation.
Military medicine
Sep 4, 2023
Total Force Fitness (TFF) metrics inform leaders at every level as they develop and evaluate policies, practices, and programs that enable soldiers, airmen, sailors, marines, guardians, and operators to achieve human performance optimization in all e...
Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning.
Bioinformatics (Oxford, England)
Aug 1, 2023
MOTIVATION: Few-shot learning that can effectively perform named entity recognition in low-resource scenarios has raised growing attention, but it has not been widely studied yet in the biomedical field. In contrast to high-resource domains, biomedic...
NeBULA: A Standardized Protocol for the Benchmarking of Robotic-based Upper Limb Neurorehabilitation.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2023
The use of robotic technologies in neurorehabilitation is growing, because they allow highly repeatable exercise protocols and patient-tailored therapies. However, there is a lack of objective methods for assessing these technologies, which makes it ...
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