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Found 24 papers, 5 papers with code
Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Generative Latent Priors
Accurate 3D mapping in endoscopy enables quantitative, holistic lesion characterization within the gastrointestinal (GI) tract, requiring reliable depth and pose estimation.
SSTFB: Leveraging self-supervised pretext learning and temporal self-attention with feature branching for real-time video polyp segmentation
Segmenting polyps in their natural video screening procedure has several challenges, such as the co-existence of imaging artefacts, motion blur, and floating debris.
Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models
Recent advances in attention-based multiple instance learning (MIL) have improved our insights into the tissue regions that models rely on to make predictions in digital pathology.
SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis
We also demonstrate that our method generalises better than all SOTA methods to unseen datasets, reporting nearly 7% improvement in our generalisability assessment.
Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring
Inflammatory bowel disease (IBD), in particular ulcerative colitis (UC), is graded by endoscopists and this assessment is the basis for risk stratification and therapy monitoring.
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy.
A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples
Multiplexed immunofluorescence provides an unprecedented opportunity for studying specific cell-to-cell and cell microenvironment interactions.
EndoUDA: A modality independent segmentation approach for endoscopy imaging
While, today most segmentation approaches are supervised and only concentrated on a single modality dataset, this work exploits to use a target-independent unsupervised domain adaptation (UDA) technique that is capable to generalize to an unseen target modality.
A multi-centre polyp detection and segmentation dataset for generalisability assessment
To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as \textit{PolypGen}) curated by a team of computational scientists and expert gastroenterologists.
Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy
We propose an end-to-end CNN-based framework for the segmentation of stones and laser fiber.
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch.
Ranked #2 on
Medical Image Segmentation
on EM
Unsupervised Adversarial Domain Adaptation For Barrett's Segmentation
Our dataset consists of a total of 871 images consisting of both source and target domains.
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks.
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies.
Microscopic fine-grained instance classification through deep attention
Fine-grained classification of microscopic image data with limited samples is an open problem in computer vision and biomedical imaging.
Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images
In this work, we propose to use a few-shot learning approach that requires less training data and can be used to predict label classes of test samples from an unseen dataset.
Endoscopy disease detection challenge 2020
What could be more important than disease detection and localization?
Semantic filtering through deep source separation on microscopy images
By their very nature microscopy images of cells and tissues consist of a limited number of object types or components.
Conv2Warp: An unsupervised deformable image registration with continuous convolution and warping
To address this problem, we propose a novel approach of learning a continuous warp of the source image.
Ink removal from histopathology whole slide images by combining classification, detection and image generation models
Removal of marker ink from these high-resolution whole slide images is non-trivial and complex problem as they contaminate different regions and in an inconsistent manner.
Efficient video indexing for monitoring disease activity and progression in the upper gastrointestinal tract
In this study, we propose to use an autoencoder for efficient video compression and fast retrieval of video images.
Endoscopy artifact detection (EAD 2019) challenge dataset
Endoscopic artifacts are a core challenge in facilitating the diagnosis and treatment of diseases in hollow organs.
A deep learning framework for quality assessment and restoration in video endoscopy
Given the widespread use of endoscopy in different clinical applications, we contend that the robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem.
Improving Whole Slide Segmentation Through Visual Context - A Systematic Study
While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology.
