A transfer-learning approach for lesion detection in endoscopic images from the urinary tract

• URL: http://arxiv.org/abs/2104.03927v1
• Date: Thu, 8 Apr 2021 17:16:12 GMT
• Title: A transfer-learning approach for lesion detection in endoscopic images from the urinary tract
• Authors: Jorge F. Lazo, Sara Moccia, Aldo Marzullo, Michele Catellani, Ottavio De Cobelli, Benoit Rosa, Michel de Mathelin, Elena De Momi
• Abstract summary: Ureteroscopy and cystoscopy are the gold standard methods to identify and treat tumors along the urinary tract. It has been reported that during a normal procedure a rate of 10-20 % of the lesions could be missed. In this work we study the implementation of 3 different Convolutional Neural Networks (CNNs) to classify images from the urinary tract with and without lesions.
• Score: 10.909933734224026
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Ureteroscopy and cystoscopy are the gold standard methods to identify and treat tumors along the urinary tract. It has been reported that during a normal procedure a rate of 10-20 % of the lesions could be missed. In this work we study the implementation of 3 different Convolutional Neural Networks (CNNs), using a 2-steps training strategy, to classify images from the urinary tract with and without lesions. A total of 6,101 images from ureteroscopy and cystoscopy procedures were collected. The CNNs were trained and tested using transfer learning in a two-steps fashion on 3 datasets. The datasets used were: 1) only ureteroscopy images, 2) only cystoscopy images and 3) the combination of both of them. For cystoscopy data, VGG performed better obtaining an Area Under the ROC Curve (AUC) value of 0.846. In the cases of ureteroscopy and the combination of both datasets, ResNet50 achieved the best results with AUC values of 0.987 and 0.940. The use of a training dataset that comprehends both domains results in general better performances, but performing a second stage of transfer learning achieves comparable ones. There is no single model which performs better in all scenarios, but ResNet50 is the network that achieves the best performances in most of them. The obtained results open the opportunity for further investigation with a view for improving lesion detection in endoscopic images of the urinary system.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging [62.346649719614]
  preoperative discrimination between T2 and T3 stages is arguably both the most challenging and clinically significant task for rectal cancer treatment. We present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes.
  arXiv  Detail & Related papers  (2022-09-13T07:10:14Z)
• 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)
• Osteoporosis Prescreening using Panoramic Radiographs through a Deep Convolutional Neural Network with Attention Mechanism [65.70943212672023]
  Deep convolutional neural network (CNN) with an attention module can detect osteoporosis on panoramic radiographs. dataset of 70 panoramic radiographs (PRs) from 70 different subjects of age between 49 to 60 was used.
  arXiv  Detail & Related papers  (2021-10-19T00:03:57Z)
• Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network [0.0]
  Deep convolutional neural networks are described to diagnose breast cancer in mammograms with similar or even superior performance to that of human experts. We present one of the best techniques that consists of two transfer learnings.
  arXiv  Detail & Related papers  (2021-10-01T22:09:59Z)
• A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture [11.457020223521605]
  We study the implementation of a Deep Neural Network which exploits the advantage of residual units in an architecture based on U-Net. We found that training on gray-scale images gives the best results obtaining mean values of Dice Score, Precision, and Recall of 0.73, 0.58, and 0.92 respectively.
  arXiv  Detail & Related papers  (2021-01-13T11:41:39Z)
• Comparison of different CNNs for breast tumor classification from ultrasound images [12.98780709853981]
  classifying benign and malignant tumors from ultrasound (US) imaging is a crucial but challenging task. We compared different Convolutional Neural Networks (CNNs) and transfer learning methods for the task of automated breast tumor classification. The best performance was obtained by fine tuning VGG-16, with an accuracy of 0.919 and an AUC of 0.934.
  arXiv  Detail & Related papers  (2020-12-28T22:54:08Z)
• A Deep Learning Study on Osteosarcoma Detection from Histological Images [6.341765152919201]
  The most common type of primary malignant bone tumor is osteosarcoma. CNNs can significantly decrease surgeon's workload and make a better prognosis of patient conditions. CNNs need to be trained on a large amount of data in order to achieve a more trustworthy performance.
  arXiv  Detail & Related papers  (2020-11-02T18:16:17Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• 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)
• Predictive modeling of brain tumor: A Deep learning approach [0.0]
  This paper presents a Convolutional Neural Network (CNN) based transfer learning approach to classify the brain MRI scans into two classes using three pre-trained models. Experimental results show that the Resnet-50 model achieves the highest accuracy and least false negative rates as 95% and zero respectively.
  arXiv  Detail & Related papers  (2019-11-06T09:27:48Z)

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.