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Found 16 papers, 12 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)
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.
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.
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.
Ranked #1 on
Classification
on TCGA
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.
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.
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.
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).
