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Found 42 papers, 25 papers with code
Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors
Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity.
Benchmarking foundation models as feature extractors for weakly-supervised computational pathology
Advancements in artificial intelligence have driven the development of numerous pathology foundation models capable of extracting clinically relevant information.
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.
Prompt Injection Attacks on Large Language Models in Oncology
Using a set of N=297 attacks, we show that all of these models are susceptible.
RadioRAG: Factual Large Language Models for Enhanced Diagnostics in Radiology Using Dynamic Retrieval Augmented Generation
We evaluate the diagnostic accuracy of various LLMs when answering radiology-specific questions with and without access to additional online information via RAG.
End-To-End Clinical Trial Matching with Large Language Models
Matching cancer patients to clinical trials is essential for advancing treatment and patient care.
On Instabilities of Unsupervised Denoising Diffusion Models in Magnetic Resonance Imaging Reconstruction
Denoising diffusion models offer a promising approach to accelerating magnetic resonance imaging (MRI) and producing diagnostic-level images in an unsupervised manner.
Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization
The Transformer model has been pivotal in advancing fields such as natural language processing, speech recognition, and computer vision.
MRSegmentator: Robust Multi-Modality Segmentation of 40 Classes in MRI and CT Sequences
Results: The model showcased high accuracy in segmenting well-defined organs, achieving Dice Similarity Coefficient (DSC) scores of 0. 97 for the right and left lungs, and 0. 95 for the heart.
Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology
We believe, that our work can serve as a proof-of-concept for more advanced LLM-agents in the medical domain.
In-context learning enables multimodal large language models to classify cancer pathology images
Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models.
Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology
Deep Learning (DL) can predict biomarkers directly from digitized cancer histology in a weakly-supervised setting.
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.
Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Data
Collectively, our results emphasize the importance of carefully training generative models on private medical imaging datasets, and examining the synthetic data to ensure patient privacy before sharing it for medical research and applications.
LongHealth: A Question Answering Benchmark with Long Clinical Documents
However, all models struggled significantly in tasks requiring the identification of missing information, highlighting a critical area for improvement in clinical data interpretation.
From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology
Hematoxylin- and eosin (H&E) stained whole-slide images (WSIs) are the foundation of diagnosis of cancer.
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From Text to Image: Exploring GPT-4Vision's Potential in Advanced Radiological Analysis across Subspecialties
The study evaluates and compares GPT-4 and GPT-4Vision for radiological tasks, suggesting GPT-4Vision may recognize radiological features from images, thereby enhancing its diagnostic potential over text-based descriptions.
Benchmarking Pathology Feature Extractors for Whole Slide Image Classification
2) Which feature extractors are best for downstream slide-level classification?
On the Impact of Cross-Domain Data on German Language Models
Traditionally, large language models have been either trained on general web crawls or domain-specific data.
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.
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.
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.
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.
MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data
As large language models (LLMs) like OpenAI's GPT series continue to make strides, we witness the emergence of artificial intelligence applications in an ever-expanding range of fields.
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.
AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge
Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible.
Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study
Methods: In this study, we developed a new fully transformer-based pipeline for end-to-end biomarker prediction from pathology slides.
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.
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).
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.
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.
An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans
Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medical applications.
Radiomic Feature Stability Analysis based on Probabilistic Segmentations
From these segmentations, we extract a high number of plausible feature vectors for each lung tumor and analyze feature variance with respect to the segmentations.
Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification
Achieving an AUROC of 0. 89, we compare the performance of our approach to Mask R-CNN and Retina U-Net as well as a radiologist.
