AIMC Topic: Benchmarking
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Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study.
Computer assisted surgery (Abingdon, England)
Feb 24, 2025
This study evaluates the performance of deep learning models in the prediction of the end time of procedures performed in the cardiac catheterization laboratory (cath lab). We employed only the clinical phases derived from video analysis as input to ...
Large-scale benchmarking and boosting transfer learning for medical image analysis.
Medical image analysis
Feb 21, 2025
Transfer learning, particularly fine-tuning models pretrained on photographic images to medical images, has proven indispensable for medical image analysis. There are numerous models with distinct architectures pretrained on various datasets using di...
A-Eval: A benchmark for cross-dataset and cross-modality evaluation of abdominal multi-organ segmentation.
Medical image analysis
Feb 14, 2025
Although deep learning has revolutionized abdominal multi-organ segmentation, its models often struggle with generalization due to training on small-scale, specific datasets and modalities. The recent emergence of large-scale datasets may mitigate th...
Benchmarking Vision Capabilities of Large Language Models in Surgical Examination Questions.
Journal of surgical education
Feb 9, 2025
OBJECTIVE: Recent studies investigated the potential of large language models (LLMs) for clinical decision making and answering exam questions based on text input. Recent developments of LLMs have extended these models with vision capabilities. These...
Initializing a Public Repository for Hosting Benchmark Datasets to Facilitate Machine Learning Model Development in Food Safety.
Journal of food protection
Feb 6, 2025
While there is clear potential for artificial intelligence (AI) and machine learning (ML) models to help improve food safety, the development and deployment of these models in the food safety domain are by and large lacking. The absence of publicly a...
Multi-institutional Knowledge-Based (KB) plan prediction benchmark models for whole breast irradiation.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Jan 16, 2025
PURPOSE: To train and validate KB prediction models by merging a large multi-institutional cohort of whole breast irradiation (WBI) plans using tangential fields.
A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort.
Scientific reports
Jan 11, 2025
Deep learning (DL) methods have demonstrated remarkable effectiveness in assisting with lung cancer risk prediction tasks using computed tomography (CT) scans. However, the lack of comprehensive comparison and validation of state-of-the-art (SOTA) mo...
Success History Adaptive Competitive Swarm Optimizer with Linear Population Reduction: Performance benchmarking and application in eye disease detection.
Computers in biology and medicine
Jan 2, 2025
Eye disease detection has achieved significant advancements thanks to artificial intelligence (AI) techniques. However, the construction of high-accuracy predictive models still faces challenges, and one reason is the deficiency of the optimizer. Thi...
A New Benchmark: Clinical Uncertainty and Severity Aware Labeled Chest X-Ray Images With Multi-Relationship Graph Learning.
IEEE transactions on medical imaging
Jan 2, 2025
Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated wit...
Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction.
PLoS computational biology
Dec 30, 2024
The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computati...
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