A Deep Convolutional Neural Network for the Detection of Polyps in Colonoscopy Images

• URL: http://arxiv.org/abs/2008.06721v1
• Date: Sat, 15 Aug 2020 13:55:44 GMT
• Title: A Deep Convolutional Neural Network for the Detection of Polyps in Colonoscopy Images
• Authors: Tariq Rahim, Syed Ali Hassan, Soo Young Shin
• Abstract summary: We propose a deep convolutional neural network based model for the computerized detection of polyps within colonoscopy images. Data augmentation techniques such as photometric and geometric distortions are adapted to overcome the obstacles faced in polyp detection.
• Score: 12.618653234201089
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computerized detection of colonic polyps remains an unsolved issue because of the wide variation in the appearance, texture, color, size, and presence of the multiple polyp-like imitators during colonoscopy. In this paper, we propose a deep convolutional neural network based model for the computerized detection of polyps within colonoscopy images. The proposed model comprises 16 convolutional layers with 2 fully connected layers, and a Softmax layer, where we implement a unique approach using different convolutional kernels within the same hidden layer for deeper feature extraction. We applied two different activation functions, MISH and rectified linear unit activation functions for deeper propagation of information and self regularized smooth non-monotonicity. Furthermore, we used a generalized intersection of union, thus overcoming issues such as scale invariance, rotation, and shape. Data augmentation techniques such as photometric and geometric distortions are adapted to overcome the obstacles faced in polyp detection. Detailed benchmarked results are provided, showing better performance in terms of precision, sensitivity, F1- score, F2- score, and dice-coefficient, thus proving the efficacy of the proposed model.

Related papers

• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp Detection [88.4359020192429]
  Existing methods either involve computationally expensive context aggregation or lack prior modeling of polyps, resulting in poor performance in challenging cases. In this paper, we propose the Enhanced CenterNet with Contrastive Learning (ECC-PolypDet), a two-stage training & end-to-end inference framework. Box-assisted Contrastive Learning (BCL) during training to minimize the intra-class difference and maximize the inter-class difference between foreground polyps and backgrounds, enabling our model to capture concealed polyps. In the fine-tuning stage, we introduce the IoU-guided Sample Re-weighting
  arXiv  Detail & Related papers  (2024-01-10T07:03:41Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Lesion-aware Dynamic Kernel for Polyp Segmentation [49.63274623103663]
  We propose a lesion-aware dynamic network (LDNet) for polyp segmentation. It is a traditional u-shape encoder-decoder structure incorporated with a dynamic kernel generation and updating scheme. This simple but effective scheme endows our model with powerful segmentation performance and generalization capability.
  arXiv  Detail & Related papers  (2023-01-12T09:53:57Z)
• BoxPolyp:Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations [79.17754846553866]
  We propose a boosted BoxPolyp model to make full use of both accurate mask and extra coarse box annotations. In practice, box annotations are applied to alleviate the over-fitting issue of previous polyp segmentation models. Our proposed model outperforms previous state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2022-12-07T07:45:50Z)
• A Hybrid Convolutional Neural Network with Meta Feature Learning for Abnormality Detection in Wireless Capsule Endoscopy Images [8.744537620217674]
  A hybrid convolutional neural network is proposed for abnormality detection in wireless capsule endoscopy images. It consists of three parallel convolutional neural networks, each with a distinctive feature learning capability. The network trio effectively handles intra-class variance and efficiently detects gastrointestinal abnormalities.
  arXiv  Detail & Related papers  (2022-07-20T09:25:57Z)
• Stepwise Feature Fusion: Local Guides Global [14.394421688712052]
  We propose a new State-Of-The-Art model for medical image segmentation, the SSFormer, which uses a pyramid Transformer encoder to improve the generalization ability of models. Our proposed Progressive Locality Decoder can be adapted to the pyramid Transformer backbone to emphasize local features and attention dispersion.
  arXiv  Detail & Related papers  (2022-03-07T10:36:38Z)
• Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation [17.8181080354116]
  We propose a feature enhancement network for accurate polyp segmentation in colonoscopy images. Specifically, the proposed network enhances the semantic information using the novel Semantic Feature Enhance Module (SFEM) The proposed approach is evaluated on five colonoscopy datasets and demonstrates superior performance compared to other state-of-the-art models.
  arXiv  Detail & Related papers  (2021-05-03T16:46:26Z)
• AG-CUResNeSt: A Novel Method for Colon Polyp Segmentation [0.0]
  This paper proposes a novel neural network architecture called AG-CUResNeSt, which enhances Coupled UNets using the robust ResNeSt backbone and attention gates. We show that our proposed method achieves state-of-the-art accuracy compared to existing methods.
  arXiv  Detail & Related papers  (2021-05-02T06:36:36Z)
• PraNet: Parallel Reverse Attention Network for Polyp Segmentation [155.93344756264824]
  We propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images. We first aggregate the features in high-level layers using a parallel partial decoder (PPD) In addition, we mine the boundary cues using a reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues.
  arXiv  Detail & Related papers  (2020-06-13T08:13:43Z)

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.