Christian Daul
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View article: Robust Federated Anomaly Detection Using Dual-Signal Autoencoders: Application to Kidney Stone Identification in Ureteroscopy
Robust Federated Anomaly Detection Using Dual-Signal Autoencoders: Application to Kidney Stone Identification in Ureteroscopy Open
This work introduces Federated Adaptive Gain via Dual Signal Trust (FedAgain), a novel federated learning algorithm designed to enhance anomaly detection in medical imaging under decentralized and heterogeneous conditions. Focusing on the …
View article: Kidney Stone Segmentation and Improved Generalization using SAM
Kidney Stone Segmentation and Improved Generalization using SAM Open
National audience
View article: Vision Transformers for Kidney Stone Image Classification: A Comparative Study with CNNs
Vision Transformers for Kidney Stone Image Classification: A Comparative Study with CNNs Open
Kidney stone classification from endoscopic images is critical for personalized treatment and recurrence prevention. While convolutional neural networks (CNNs) have shown promise in this task, their limited ability to capture long-range de…
View article: Machine learning–based classification of spatially resolved diffuse reflectance and autofluorescence spectra acquired on human skin for actinic keratoses and skin carcinoma diagnostics aid
Machine learning–based classification of spatially resolved diffuse reflectance and autofluorescence spectra acquired on human skin for actinic keratoses and skin carcinoma diagnostics aid Open
Such levels of classification accuracy are promising as they are comparable to those obtained by general practitioners in KC screening.
View article: Transformer-Based Illumination Invariant Self-Supervised Monocular Depth Estimation in Endoscopy
Transformer-Based Illumination Invariant Self-Supervised Monocular Depth Estimation in Endoscopy Open
View article: Corrections to “On the In Vivo Recognition of Kidney Stones Using Machine Learning”
Corrections to “On the In Vivo Recognition of Kidney Stones Using Machine Learning” Open
Presents corrections to the paper, (Corrections to “On the In Vivo Recognition of Kidney Stones Using Machine Learning”).
View article: Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition
Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition Open
Deep learning developments have improved medical imaging diagnoses dramatically, increasing accuracy in several domains. Nonetheless, obstacles continue to exist because of the requirement for huge datasets and legal limitations on data ex…
View article: EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth Prediction
EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth Prediction Open
Accurate depth estimation in endoscopy is vital for successfully implementing computer vision pipelines for various medical procedures and CAD tools. In this paper, we present the EndoDepth benchmark, an evaluation framework designed to as…
View article: Improving Prototypical Parts Abstraction for Case-Based Reasoning Explanations Designed for the Kidney Stone Type Recognition
Improving Prototypical Parts Abstraction for Case-Based Reasoning Explanations Designed for the Kidney Stone Type Recognition Open
The in-vivo identification of the kidney stone types during an ureteroscopy would be a major medical advance in urology, as it could reduce the time of the tedious renal calculi extraction process, while diminishing infection risks. Furthe…
View article: Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition
Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition Open
Currently, the Morpho-Constitutional Analysis (MCA) is the de facto approach\nfor the etiological diagnosis of kidney stone formation, and it is an important\nstep for establishing personalized treatment to avoid relapses. More recently,\n…
View article: Validation of a White Light and Fluorescence Augmented Panoramic Endoscopic Imaging System on a Bimodal Bladder Wall Experimental Model
Validation of a White Light and Fluorescence Augmented Panoramic Endoscopic Imaging System on a Bimodal Bladder Wall Experimental Model Open
Background: Fluorescence visualization of pathologies, primarily neoplasms in human internal cavities, is one of the most popular forms of diagnostics during endoscopic examination in medical practice. Currently, visualization can be perfo…
View article: Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge Open
View article: On the In Vivo Recognition of Kidney Stones Using Machine Learning
On the In Vivo Recognition of Kidney Stones Using Machine Learning Open
Determining the type of kidney stones allows urologists to prescribe a treatment to avoid the recurrence of renal lithiasis. An automated in-vivo image-based classification method would be an important step towards an immediate identificat…
View article: Automated endoscopic stone recognition using a multi-view fusion approach and a two-step transfer learning
Automated endoscopic stone recognition using a multi-view fusion approach and a two-step transfer learning Open
International audience
View article: A metric learning approach for endoscopic kidney stone identification
A metric learning approach for endoscopic kidney stone identification Open
Several Deep Learning (DL) methods have recently been proposed for an automated identification of kidney stones during an ureteroscopy to enable rapid therapeutic decisions. Even if these DL approaches led to promising results, they are ma…
View article: Photometric and Monte-Carlo modeling unified approach for the calculation of spatially-resolved correction coefficients linking simulated and experimental diffuse reflectance spectra
Photometric and Monte-Carlo modeling unified approach for the calculation of spatially-resolved correction coefficients linking simulated and experimental diffuse reflectance spectra Open
The estimation of skin optical properties by means of inverse problem solving from spatially resolved diffuse reflectance (SR-DR) spectra is one way to exploit the acquired clinical signals. This method requires the comparison between the …
View article: Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations
Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations Open
Identifying the type of kidney stones can allow urologists to determine their cause of formation, improving the prescription of appropriate treatments to diminish future relapses. Currently, the associated ex-vivo diagnosis (known as Morph…
View article: Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction
Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction Open
This contribution shows how an appropriate image pre-processing can improve a deep-learning based 3D reconstruction of colon parts. The assumption is that, rather than global image illumination corrections, local under- and over-exposures …
View article: Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning
Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning Open
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…
View article: A multi-centre polyp detection and segmentation dataset for generalisability assessment
A multi-centre polyp detection and segmentation dataset for generalisability assessment Open
Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp’s number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to …
View article: Technical Program
Technical Program Open
A survey of approaches in
View article: Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies
Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies Open
This contribution presents a deep learning method for the extraction and fusion of information relating to kidney stone fragments acquired from different viewpoints of the endoscope. Surface and section fragment images are jointly used dur…
View article: Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging
Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging Open
Endoscopy is the most widely used imaging technique for the diagnosis of cancerous lesions in hollow organs. However, endoscopic images are often affected by illumination artefacts: image parts may be over- or underexposed according to the…
View article: Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning
Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning Open
Knowing the cause of kidney stone formation is crucial to establish treatments that prevent recurrence. There are currently different approaches for determining the kidney stone type. However, the reference ex-vivo identification procedure…
View article: Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams
Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams Open
International audience
View article: Comparing feature fusion strategies for Deep Learning-based kidney stone identification
Comparing feature fusion strategies for Deep Learning-based kidney stone identification Open
International audience
View article: A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models
A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models Open
Endoscopy is the most widely used medical technique for cancer and polyp detection inside hollow organs. However, images acquired by an endoscope are frequently affected by illumination artefacts due to the enlightenment source orientation…
View article: Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams
Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams Open
In this contribution we use an ensemble deep-learning method for combining the prediction of two individual one-stage detectors (i.e., YOLOv4 and Yolact) with the aim to detect artefacts in endoscopic images. This ensemble strategy enabled…
View article: Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images
Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images Open
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. However, currently, the associated ex-vivo diagnosis (kn…
View article: Comparing feature fusion strategies for Deep Learning-based kidney stone identification
Comparing feature fusion strategies for Deep Learning-based kidney stone identification Open
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. Our approach was specifically designed to mimic the…