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Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods
Research article (Abdominal Radiology, 2024) · cited 21× · AI/ML
Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods
Summary
Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods is a scholarly article[1].
Key Facts
Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods's instance of is recorded as scholarly article[2].
References
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APA4ort.xyz Knowledge Graph. (2026). Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods. Retrieved May 24, 2026, from https://4ort.xyz/entity/assessing-the-robustness-of-a-machine-learning-model-for-early-detection-of-pancreatic-adenocarcinoma-pda-evaluating-res
MLA“Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/assessing-the-robustness-of-a-machine-learning-model-for-early-detection-of-pancreatic-adenocarcinoma-pda-evaluating-res.
BibTeX@misc{4ortxyz_assessing-the-robustness-of-a-machine-learning-model-for-early-detection-of-pancreatic-adenocarcinoma-pda-evaluating-res_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods}}, year = {2026}, url = {https://4ort.xyz/entity/assessing-the-robustness-of-a-machine-learning-model-for-early-detection-of-pancreatic-adenocarcinoma-pda-evaluating-res}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods — https://4ort.xyz/entity/assessing-the-robustness-of-a-machine-learning-model-for-early-detection-of-pancreatic-adenocarcinoma-pda-evaluating-res (retrieved 2026-05-24)