Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition

• URL: http://arxiv.org/abs/2409.13409v1
• Date: Fri, 20 Sep 2024 11:19:08 GMT
• Title: Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition
• Authors: Ruben Gonzalez-Perez, Francisco Lopez-Tiro, Ivan Reyes-Amezcua, Eduardo Falcon-Morales, Rosa-Maria Rodriguez-Gueant, Jacques Hubert, Michel Daudon, Gilberto Ochoa-Ruiz, Christian Daul,
• Abstract summary: 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)
• Score: 0.9480662172227129
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Currently, the Morpho-Constitutional Analysis (MCA) is the de facto approach for the etiological diagnosis of kidney stone formation, and it is an important step for establishing personalized treatment to avoid relapses. More recently, research has focused on performing such tasks intra-operatively, an approach known as Endoscopic Stone Recognition (ESR). Both methods rely on features observed in the surface and the section of kidney stones to separate the analyzed samples into several sub-groups. However, given the high intra-observer variability and the complex operating conditions found in ESR, there is a lot of interest in using AI for computer-aided diagnosis. However, current AI models require large datasets to attain a good performance and for generalizing to unseen distributions. This is a major problem as large labeled datasets are very difficult to acquire, and some classes of kidney stones are very rare. Thus, in this paper, we present a method based on diffusion as a way of augmenting pre-existing ex-vivo kidney stone datasets. Our aim is to create plausible diverse kidney stone images that can be used for pre-training models using ex-vivo data. We show that by mixing natural and synthetic images of CCD images, it is possible to train models capable of performing very well on unseen intra-operative data. Our results show that is possible to attain an improvement of 10% in terms of accuracy compared to a baseline model pre-trained only on ImageNet. Moreover, our results show an improvement of 6% for surface images and 10% for section images compared to a model train on CCD images only, which demonstrates the effectiveness of using synthetic images.

Related papers

• Rapid hyperspectral photothermal mid-infrared spectroscopic imaging from sparse data for gynecologic cancer tissue subtyping [3.550171634694342]
  Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude.
  arXiv  Detail & Related papers  (2024-02-28T00:57:35Z)
• Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation [47.250147322130545]
  Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. We present a novel multi-modal deep neural network framework for generating chest X-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes.
  arXiv  Detail & Related papers  (2023-11-18T14:37:53Z)
• Augmenting medical image classifiers with synthetic data from latent diffusion models [12.077733447347592]
  We show that latent diffusion models can scalably generate images of skin disease. We generate and analyze a new dataset of 458,920 synthetic images produced using several generation strategies.
  arXiv  Detail & Related papers  (2023-08-23T22:34:49Z)
• Realistic Data Enrichment for Robust Image Segmentation in Histopathology [2.248423960136122]
  We propose a new approach, based on diffusion models, which can enrich an imbalanced dataset with plausible examples from underrepresented groups. Our method can simply expand limited clinical datasets making them suitable to train machine learning pipelines.
  arXiv  Detail & Related papers  (2023-04-19T09:52:50Z)
• Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data [2.715884199292287]
  We tackle the tumor localization and detection problem under the setting of few labeled whole slide images. Our method achieves similar performance by less than ten percent of training samples on the public Camelyon16 dataset.
  arXiv  Detail & Related papers  (2023-03-18T06:07:10Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• 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)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• 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)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.