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08/27/2023
Empowering Clinicians and Democratizing Data Science: Large Language Models Automate Machine Learning for Clinical Studies
A knowledge gap persists between Machine Learning (ML) developers (e.g.,...
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06/10/2023
Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy
Developing robust and effective artificial intelligence (AI) models in m...
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05/11/2023
Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs
Computed Tomography (CT) scans provide detailed and accurate information...
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05/11/2023
Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers
Whole-Slide Imaging allows for the capturing and digitization of high-re...
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04/18/2023
Fibroglandular Tissue Segmentation in Breast MRI using Vision Transformers – A multi-institutional evaluation
Accurate and automatic segmentation of fibroglandular tissue in breast M...
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02/03/2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology
Artificial intelligence (AI) models are increasingly used in the medical...
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12/18/2022
Medical Diagnosis with Large Scale Multimodal Transformers: Leveraging Diverse Data for More Accurate Diagnosis
Multimodal deep learning has been used to predict clinical endpoints and...
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12/14/2022
Diffusion Probabilistic Models beat GANs on Medical Images
The success of Deep Learning applications critically depends on the qual...
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11/24/2022
Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels
Artificial intelligence (AI) methods are revolutionizing medical image a...
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11/07/2022
Medical Diffusion – Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
Recent advances in computer vision have shown promising results in image...
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11/22/2021
Predicting Osteoarthritis Progression in Radiographs via Unsupervised Representation Learning
Osteoarthritis (OA) is the most common joint disorder affecting substant...
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11/25/2020
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
Unmasking the decision-making process of machine learning models is esse...
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06/14/2019
