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Found 14 papers, 9 papers with code
A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation
We then present ColonTCN, a learning-based architecture that employs custom temporal convolutional blocks designed to efficiently capture long temporal dependencies for the temporal segmentation of colonoscopy videos.
Hard-Attention Gates with Gradient Routing for Endoscopic Image Computing
To address overfitting and enhance model generalization in gastroenterological polyp size assessment, our study introduces Feature-Selection Gates (FSG) or Hard-Attention Gates (HAG) alongside Gradient Routing (GR) for dynamic feature selection.
REAL-Colon: A dataset for developing real-world AI applications in colonoscopy
Detection and diagnosis of colon polyps are key to preventing colorectal cancer.
Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection
Object detection has witnessed significant progress by relying on large, manually annotated datasets.
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images
Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart.
VS-Net: Variable splitting network for accelerated parallel MRI reconstruction
In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data.
Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation
Deep unsupervised domain adaptation (UDA) aims to improve the performance of a deep neural network model on a target domain, using solely unlabelled target domain data and labelled source domain data.
Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models
At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space.
3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders
Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology.
Generalising Deep Learning MRI Reconstruction across Different Domains
We look into robustness of deep learning based MRI reconstruction when tested on unseen contrasts and organs.
Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images.
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation.
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
However, current approaches to the diagnosis of cardiovascular diseases often rely on subjective human assessment as well as manual analysis of medical images.
