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Found 10 papers, 4 papers with code
Towards General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks
To measure the effectiveness and generalizability of DINOv2's feature representations, we analyze the model across medical image analysis tasks including disease classification and organ segmentation on both 2D and 3D images, and under different settings like kNN, few-shot learning, linear-probing, end-to-end fine-tuning, and parameter-efficient fine-tuning.
Contrastive Self-Supervised Learning for Spatio-Temporal Analysis of Lung Ultrasound Videos
Self-supervised learning (SSL) methods have shown promise for medical imaging applications by learning meaningful visual representations, even when the amount of labeled data is limited.
Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIs
Generative AI models hold great potential in creating synthetic brain MRIs that advance neuroimaging studies by, for example, enriching data diversity.
LSOR: Longitudinally-Consistent Self-Organized Representation Learning
Interpretability is a key issue when applying deep learning models to longitudinal brain MRIs.
Weakly Semi-Supervised Detection in Lung Ultrasound Videos
Frame-by-frame annotation of bounding boxes by clinical experts is often required to train fully supervised object detection models on medical video data.
Self-Supervised Longitudinal Neighbourhood Embedding
Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and function caused by aging or neurological diseases.
Representation Disentanglement for Multi-modal brain MR Analysis
To address this issue, we propose a margin loss that regularizes the similarity in relationships of the representations across subjects and modalities.
Longitudinal Pooling & Consistency Regularization to Model Disease Progression from MRIs
Many neurological diseases are characterized by gradual deterioration of brain structure and function.
Accurate Tissue Interface Segmentation via Adversarial Pre-Segmentation of Anterior Segment OCT Images
To the best of our knowledge, this is the first approach to remove severe specular artifacts and speckle noise patterns (prior to the shallowest interface) that affects the interpretation of anterior segment OCT datasets, thereby resulting in the accurate segmentation of the shallowest tissue interface.
Task-GAN for Improved GAN based Image Restoration
Deep Learning (DL) algorithms based on Generative Adversarial Network (GAN) have demonstrated great potentials in computer vision tasks such as image restoration.
