Gastrointestinal Polyps and Tumors Detection Based on Multi-scale Feature-fusion with WCE Sequences

• URL: http://arxiv.org/abs/2204.01012v1
• Date: Sun, 3 Apr 2022 07:24:50 GMT
• Title: Gastrointestinal Polyps and Tumors Detection Based on Multi-scale Feature-fusion with WCE Sequences
• Authors: Zhuo Falin, Liu Haihua and Pan Ning
• Abstract summary: This paper proposes a textbfTwo-stage textbfMulti-scale textbfFeature-fusion learning network(textbfTMFNet) to automatically detect small intestinal polyps and tumors. We used 22,335 WCE images in the experiment, with a total of 123,092 lesion regions used to train the detection framework of this paper.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wireless Capsule Endoscopy(WCE) has been widely used for the screening of gastrointestinal(GI) diseases, especially the small intestine, due to its advantages of non-invasive and painless imaging of the entire digestive tract.However, the huge amount of image data captured by WCE makes manual reading a process that requires a huge amount of tasks and can easily lead to missed detection and false detection of lesions.Therefore, In this paper, we propose a \textbf{T}wo-stage \textbf{M}ulti-scale \textbf{F}eature-fusion learning network(\textbf{TMFNet}) to automatically detect small intestinal polyps and tumors in WCE image sequences. Specifically, TMFNet consists of lesion detection network and lesion identification network. Among them, the former improves the feature extraction module and detection module based on the traditional Faster R-CNN network, and readjusts the parameters of the anchor in the region proposal network(RPN) module;the latter combines residual structure and feature pyramid structure are used to build a small intestinal lesion recognition network based on feature fusion, for reducing the false positive rate of the former and improve the overall accuracy.We used 22,335 WCE images in the experiment, with a total of 123,092 lesion regions used to train the detection framework of this paper. In the experiment, the detection framework is trained and tested on the real WCE image dataset provided by the hospital gastroenterology department. The sensitivity, false positive and accuracy of the final model on the RPM are 98.81$\%$, 7.43$\%$ and 92.57$\%$, respectively.Meanwhile,the corresponding results on the lesion images were 98.75$\%$, 5.62$\%$ and 94.39$\%$. The algorithm model proposed in this paper is obviously superior to other detection algorithms in detection effect and performance

Related papers

• Mediastinal Lymph Node Detection and Segmentation Using Deep Learning [1.7188280334580195]
  In clinical practice, computed tomography (CT) and positron emission tomography (PET) imaging detect abnormal lymph nodes (LNs) Deep convolutional neural networks frequently segment items in medical photographs. A well-established deep learning technique UNet was modified using bilinear and total generalized variation (TGV) based up strategy to segment and detect mediastinal lymph nodes. The modified UNet maintains texture discontinuities, selects noisy areas, searches appropriate balance points through backpropagation, and recreates image resolution.
  arXiv  Detail & Related papers  (2022-11-24T02:55:20Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection [19.165942326142538]
  We propose a robust universal lesion detection (ULD) network that can detect lesions across all organs of the body by training on a single dataset, DeepLesion. We analyze CT-slices of varying intensities, generated using a novel convolution augmented multi-head self-attention module. We evaluate the efficacy of our network on the publicly available DeepLesion dataset which comprises of approximately 32K CT scans with annotated lesions across all organs of the body.
  arXiv  Detail & Related papers  (2022-03-14T06:54:28Z)
• CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images [58.720142291102135]
  We propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN) For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones.
  arXiv  Detail & Related papers  (2021-11-16T15:03:42Z)
• Universal Lesion Detection in CT Scans using Neural Network Ensembles [5.341593824515018]
  A prerequisite for lesion sizing is their detection, as it promotes the downstream assessment of tumor spread. We propose the use of state-of-the-art detection neural networks to flag suspicious lesions present in the NIH DeepLesion dataset for sizing. We construct an ensemble of the best detection models to localize lesions for sizing with a precision of 65.17% and sensitivity of 91.67% at 4 FP per image.
  arXiv  Detail & Related papers  (2021-11-09T00:11:01Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet) [3.0929226049096217]
  We present a fully automatic end-to-end esophageal tumor segmentation method based on convolutional neural networks (CNNs) The proposed network, called Dilated Dense Attention Unet (DDAUnet), leverages spatial and channel attention in each dense block to selectively concentrate on determinant feature maps and regions.
  arXiv  Detail & Related papers  (2020-12-06T11:42:52Z)
• Multiscale Detection of Cancerous Tissue in High Resolution Slide Scans [0.0]
  We present an algorithm for multi-scale tumor (chimeric cell) detection in high resolution slide scans. Our approach modifies the effective receptive field at different layers in a CNN so that objects with a broad range of varying scales can be detected in a single forward pass.
  arXiv  Detail & Related papers  (2020-10-01T18:56:46Z)
• 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)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)
• ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection [90.13718478362337]
  We introduce a novel ElixirNet that includes three components: 1) TruncatedRPN balances positive and negative data for false positive reduction; 2) Auto-lesion Block is automatically customized for medical images to incorporate relation-aware operations among region proposals; and 3) Relation transfer module incorporates the semantic relationship. Experiments on DeepLesion and Kits19 prove the effectiveness of ElixirNet, achieving improvement of both sensitivity and precision over FPN with fewer parameters.
  arXiv  Detail & Related papers  (2020-03-03T05:29:49Z)

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.