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Found 11 papers, 2 papers with code
Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data
Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data.
Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection
For the second cohort, follow-up data of 311 patients was used to examine progression prediction capabilities.
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
A human-in-the-loop annotation workflow was employed, leveraging cross-modality transfer learning from an existing CT segmentation model to segment 40 anatomical structures.
MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain
This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain.
What Does DALL-E 2 Know About Radiology?
Generative models such as DALL-E 2 could represent a promising future tool for image generation, augmentation, and manipulation for artificial intelligence research in radiology provided that these models have sufficient medical domain knowledge.
U-Net-based Lung Thickness Map for Pixel-level Lung Volume Estimation of Chest X-rays
Purpose: We aimed to estimate the total lung volume (TLV) from real and synthetic frontal X-ray radiographs on a pixel level using lung thickness maps generated by a U-Net.
Tracked 3D Ultrasound and Deep Neural Network-based Thyroid Segmentation reduce Interobserver Variability in Thyroid Volumetry
Conclusion: Tracked 3D ultrasound combined with a CNN segmentation significantly reduces interobserver variability in thyroid volumetry and increases the accuracy of the measurements with shorter acquisition times.
NeuralDP Differentially private neural networks by design
The application of differential privacy to the training of deep neural networks holds the promise of allowing large-scale (decentralized) use of sensitive data while providing rigorous privacy guarantees to the individual.
U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction
Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality.
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty
We show that differentially private stochastic gradient descent (DP-SGD) can yield poorly calibrated, overconfident deep learning models.
3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware
Our model performed comparable to previously published 3D U-Net architectures, achieving a mean Dice score of 0. 679 on the tuning dataset, 0. 648 on the Coronacases dataset and 0. 405 on the MosMed dataset.
