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Found 12 papers, 4 papers with code
Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition
The application of Tucker decomposition to the TS model substantially reduced the model parameters and FLOPs across various compression rates, with limited loss in segmentation accuracy.
Unreading Race: Purging Protected Features from Chest X-ray Embeddings
Materials and Methods: An orthogonalization is utilized to remove the influence of protected features (e. g., age, sex, race) in chest radiograph embeddings, ensuring feature-independent results.
Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning
In this work, we explore the potential of weak supervision of a deep learning-based label prediction model, using a rule-based labeler.
Higher Chest X-ray Resolution Improves Classification Performance
Deep learning models for image classification are often trained at a resolution of 224 x 224 pixels for historical and efficiency reasons.
WindowNet: Learnable Windows for Chest X-ray Classification
Finally, we propose and evaluate WindowNet, a model that learns optimal window settings, and show that it significantly improves performance compared to the baseline model without windowing.
German CheXpert Chest X-ray Radiology Report Labeler
This study aimed to develop an algorithm to automatically extract annotations for chest X-ray classification models from German thoracic radiology reports.
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction
Undersampling is a common method in Magnetic Resonance Imaging (MRI) to subsample the number of data points in k-space, reducing acquisition times at the cost of decreased image quality.
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis
While recent advances in large-scale foundational models show promising results, their application to the medical domain has not yet been explored in detail.
ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports
In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT.
A knee cannot have lung disease: out-of-distribution detection with in-distribution voting using the medical example of chest X-ray classification
The proposed IDV approach trained on ID (chest X-ray 14) and OOD data (IRMA and ImageNet) achieved, on average, 0. 999 OOD AUC across the three data sets, surpassing all other OOD detection methods.
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation
The application of deep learning in survival analysis (SA) allows utilizing unstructured and high-dimensional data types uncommon in traditional survival methods.
Survival-oriented embeddings for improving accessibility to complex data structures
We propose a hazard-regularized variational autoencoder that supports straightforward interpretation of deep neural architectures in the context of survival analysis, a field highly relevant in healthcare.
