On the in vivo recognition of kidney stones using machine learning

• URL: http://arxiv.org/abs/2201.08865v2
• Date: Thu, 24 Aug 2023 21:58:18 GMT
• Title: On the in vivo recognition of kidney stones using machine learning
• Authors: Francisco Lopez-Tiro, Vincent Estrade, Jacques Hubert, Daniel Flores-Araiza, Miguel Gonzalez-Mendoza, Gilberto Ochoa-Ruiz, Christian Daul
• Abstract summary: This paper compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures. It is also shown that choosing an appropriate colour space and texture features allows a shallow machine learning method to approach closely the performances of the most promising deep-learning methods.
• Score: 1.6273083168563973
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Determining the type of kidney stones allows urologists to prescribe a treatment to avoid recurrence of renal lithiasis. An automated in-vivo image-based classification method would be an important step towards an immediate identification of the kidney stone type required as a first phase of the diagnosis. In the literature it was shown on ex-vivo data (i.e., in very controlled scene and image acquisition conditions) that an automated kidney stone classification is indeed feasible. 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. This contribution details the database construction and the design of the tested kidney stones classifiers. Even if the best results were obtained by the Inception v3 architecture (weighted precision, recall and F1-score of 0.97, 0.98 and 0.97, respectively), it is also shown that choosing an appropriate colour space and texture features allows a shallow machine learning method to approach closely the performances of the most promising deep-learning methods (the XGBoost classifier led to weighted precision, recall and F1-score values of 0.96). This paper is the first one that explores the most discriminant features to be extracted from images acquired during ureteroscopies.

Related papers

• Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition [0.9480662172227129]
  Currently, the Morpho-Constitutional Analysis (MCA) is the de facto approach for the etiological diagnosis of kidney stone formation. More recently, research has focused on performing such tasks intra-operatively, an approach known as Endoscopic Stone Recognition (ESR)
  arXiv  Detail & Related papers  (2024-09-20T11:19:08Z)
• Improving Prototypical Parts Abstraction for Case-Based Reasoning Explanations Designed for the Kidney Stone Type Recognition [2.5506430540951763]
  The in-vivo identification of the kidney stone types during an ureteroscopy would be a major medical advance in urology. Several deep learning (DL) models have recently been proposed to automatically recognize the kidney stone types using ureteroscopic images. This contribution proposes a case-based reasoning DL model which uses prototypical parts (PPs) and generates local and global descriptors.
  arXiv  Detail & Related papers  (2024-09-19T16:27:32Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders [50.689585476660554]
  We propose a new fine-tuning strategy that includes positive-pair loss relaxation and random sentence sampling. Our approach consistently improves overall zero-shot pathology classification across four chest X-ray datasets and three pre-trained models.
  arXiv  Detail & Related papers  (2022-12-14T06:04:18Z)
• Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning [0.8431877864777444]
  The proposed approach transfers knowledge learned on a set of images of kidney stones acquired with a CCD camera to a final model that classifies images from endoscopic images. The results show that learning features from different domains with similar information helps to improve the performance of a model that performs classification in real conditions.
  arXiv  Detail & Related papers  (2022-10-24T23:22:22Z)
• Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis [48.64462717254158]
  We developed a self-supervised contrastive learning approach, EchoCLR, to catered to echocardiogram videos. When fine-tuned on small portions of labeled data, EchoCLR pretraining significantly improved classification performance for left ventricular hypertrophy (LVH) and aortic stenosis (AS) EchoCLR is unique in its ability to learn representations of medical videos and demonstrates that SSL can enable label-efficient disease classification from small, labeled datasets.
  arXiv  Detail & Related papers  (2022-07-23T19:17:26Z)
• Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images [0.9236074230806579]
  Currently, the associated ex-vivo diagnosis (known as morpho-constitutional analysis, MCA) is time-consuming, expensive, and requires a great deal of experience. Machine learning methods have been developed for in-vivo endoscopic stone recognition. Our proposal suggests a classification for a kidney stone patch image and provides explanations in a similar way as those used on the MCA method.
  arXiv  Detail & Related papers  (2022-06-01T06:32:31Z)
• A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images [58.17507437526425]
  Collateral circulation results from specialized anastomotic channels which provide oxygenated blood to regions with compromised blood flow. The actual grading is mostly done through manual inspection of the acquired images. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data.
  arXiv  Detail & Related papers  (2021-10-24T18:58:40Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Assessing deep learning methods for the identification of kidney stones in endoscopic images [0.0]
  Knowing the type (i.e., the biochemical composition) of kidney stones is crucial to prevent relapses. During ureteroscopies, kidney stones are fragmented, extracted from the urinary tract, and their composition is determined using a morpho-constitutional analysis. This paper discusses and compares five classification methods including deep convolutional neural networks (DCNN)-based approaches and traditional (non DCNN-based) ones.
  arXiv  Detail & Related papers  (2021-03-01T17:31:01Z)
• A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability [76.64661091980531]
  People with diabetes are at risk of developing diabetic retinopathy (DR) Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading. This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists.
  arXiv  Detail & Related papers  (2020-08-22T07:48:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.