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Found 17 papers, 11 papers with code
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.
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.
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 for chest radiograph diagnosis regarding accuracy and fairness compared to non-private training.
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.
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).
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.
