main webpage W Topic Machine Learning Proceedings of the AAAI Conference on Artificial Intelligence • Vol 37 • No 13 AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification 2023 Accurately predicting the volume of amniotic fluid is fundamental to assessing pregnancy risks, though the task usually requires many hours of laborious work by medical experts. In this paper, we present AmnioML, a machine learning solution that leverages dee… Article Open Machine Learning Open Article Page

Machine Learning

Study of algorithms that improve automatically through experience Machine learning ( ML ) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Critical Symbolic Virtual Narrative Open Machine Learning Open Article Page

Exploring foci of: Proceedings of the AAAI Conference on Artificial Intelligence • Vol 37 • No 13 AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification 2023 Accurately predicting the volume of amniotic fluid is fundamental to assessing pregnancy risks, though the task usually requires many hours of laborious work by medical experts. In this paper, we present AmnioML, a machine learning solution that leverages deep learning and conformal prediction to output fast and accurate volume estimates and segmentation masks from fetal MRIs with Dice coefficient over 0.9. Also, we make available a novel, curated dataset for fetal MRIs with 853 exams and benchmark the performance… Open Article Page

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Explore Machine Learning W Topic Machine Learning Proceedings of the AAAI Conference on Artificial Intelligence • Vol 37 • No 13 AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification 2023 Accurately predicting the volume of amniotic fluid is fundamental to assessing pregnancy risks, though the task usually requires many hours of laborious work by medical experts. In this paper, we present AmnioML, a machine learning solution that leverages dee… Article Critical Symbolic Virtual Narrative Open Machine Learning Open Article Page