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Found 21 papers, 2 papers with code
A metric learning approach for endoscopic kidney stone identification
The proposed Guided Deep Metric Learning approach is based on a novel architecture which was designed to learn data representations in an improved way.
Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations
Using PPs in the classification task enables case-based reasoning explanations for such output, thus making the model interpretable.
Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction
This contribution shows how an appropriate image pre-processing can improve a deep-learning based 3D reconstruction of colon parts.
Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning
This contribution presents a deep-learning method for extracting and fusing image information acquired from different viewpoints, with the aim to produce more discriminant object features for the identification of the type of kidney stones seen in endoscopic images.
Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies
Moreover, in comparison to the state-of-the-art, the fusion of the deep features improved the overall results up to 11% in terms of kidney stone classification accuracy.
Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging
Endoscopy is the most widely used imaging technique for the diagnosis of cancerous lesions in hollow organs.
Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning
Finally, in comparison to models that are trained from scratch or by initializing ImageNet weights, the obtained results suggest that the two-step approach extracts features improving the identification of kidney stones in endoscopic images.
A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models
Endoscopy is the most widely used medical technique for cancer and polyp detection inside hollow organs.
Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams
Extensive experiments show the superiority, in terms of mean average precision, of the ensemble approach over the individual models and previous works in the state of the art.
Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images
Identifying the type of kidney stones can allow urologists to determine their formation cause, improving the early prescription of appropriate treatments to diminish future relapses.
Comparing feature fusion strategies for Deep Learning-based kidney stone identification
This contribution presents a deep-learning method for extracting and fusing image information acquired from different viewpoints with the aim to produce more discriminant object features.
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification
Deep learning has shown great promise in diverse areas of computer vision, such as image classification, object detection and semantic segmentation, among many others.
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.
On the in vivo recognition of kidney stones using machine learning
This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculi types acquired with an endoscope during standard ureteroscopies.
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.
Assessing deep learning methods for the identification of kidney stones in endoscopic images
Knowing the type (i. e., the biochemical composition) of kidney stones is crucial to prevent relapses with an appropriate treatment.
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.
Endoscopy disease detection challenge 2020
What could be more important than disease detection and localization?
Endoscopy artifact detection (EAD 2019) challenge dataset
Endoscopic artifacts are a core challenge in facilitating the diagnosis and treatment of diseases in hollow organs.
Construction of extended 3D field of views of the internal bladder wall surface: a proof of concept
In this paper, we propose a 3D image mosaicing algorithm guided by 2D cystoscopic video-image registration for obtaining textured FOV mosaics.
Comparative study of image registration techniques for bladder video-endoscopy
Bladder cancer is widely spread in the world.
