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Found 19 papers, 11 papers with code
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
We introduce the Medical Slice Transformer (MST) framework to adapt 2D self-supervised models for 3D medical image analysis.
Ranked #5 on
Lung Nodule Classification
on LIDC-IDRI
(AUC metric, using extra
training data)
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.
Safety-Critical Stabilization of Force-Controlled Nonholonomic Mobile Robots
Our main results guarantee both safety and local asymptotic stability for the closed-loop system.
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.
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 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.
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.
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.
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.
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.
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).
Novice Type Error Diagnosis with Natural Language Models
Strong static type systems help programmers eliminate many errors without much burden of supplying type annotations.
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.
