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Found 17 papers, 15 papers with code
An Ordinal Regression Framework for a Deep Learning Based Severity Assessment for Chest Radiographs
This study investigates the application of ordinal regression methods for categorizing disease severity in chest radiographs.
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models
So far, the impact of training strategy, i. e., local versus collaborative, on the diagnostic on-domain and off-domain performance of AI models interpreting chest radiographs has not been assessed.
Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining
Current techniques for evaluating deep learning models cannot visualize confounding factors at a diagnostic level.
Medical Foundation Models are Susceptible to Targeted Misinformation Attacks
We validate our findings in a set of 1, 038 incorrect biomedical facts.
Large Language Models Streamline Automated Machine Learning for Clinical Studies
A knowledge gap persists between machine learning (ML) developers (e. g., data scientists) and practitioners (e. g., clinicians), hampering the full utilization of ML for clinical data analysis.
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images
In this study, we explored if SSL for pre-training on non-medical images can be applied to chest radiographs and how it compares to supervised pre-training on non-medical images and on medical images.
Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy
We specifically investigate the performance of models trained with DP as compared to models trained without DP on data from institutions that the model had not seen during its training (i. e., external validation) - the situation that is reflective of the clinical use of AI models.
Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs
X-rays are widely available and even if the CT reconstructed from these radiographs is not a replacement of a complete CT in the diagnostic setting, it might serve to spare the patients from radiation where a CT is only acquired for rough measurements such as determining organ size.
Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers
However, while the conventional transformer allows for a simultaneous processing of a large set of input tokens, the computational demand scales quadratically with the number of input tokens and thus quadratically with the number of image patches.
Fibroglandular Tissue Segmentation in Breast MRI using Vision Transformers -- A multi-institutional evaluation
Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement.
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
In this work, we evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training.
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 diagnoses from clinical routine data.
Diffusion Probabilistic Models beat GANs on Medical Images
The success of Deep Learning applications critically depends on the quality and scale of the underlying training data.
Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels
Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL).
Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
Furthermore, we demonstrate that synthetic images can be used in a self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (dice score 0. 91 vs. 0. 95 without vs. with synthetic data).
Image prediction of disease progression by style-based manifold extrapolation
We present a generic solution for this problem by a methodology that allows the prediction of progression risk and morphology in individuals using a latent extrapolation optimization approach.
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
Contrary to previous research on adversarially trained models, we found that the accuracy of such models was equal to standard models when sufficiently large datasets and dual batch norm training were used.
