Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis

• URL: http://arxiv.org/abs/2007.05914v1
• Date: Sun, 12 Jul 2020 05:24:08 GMT
• Title: Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis
• Authors: Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes
• Abstract summary: We propose a two-stream model for endoscopic image analysis. Our model fuses two streams of deep feature inputs by mapping their inherent relations through a novel relational network model.
• Score: 45.19890687786009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automating the analysis of imagery of the Gastrointestinal (GI) tract captured during endoscopy procedures has substantial potential benefits for patients, as it can provide diagnostic support to medical practitioners and reduce mistakes via human error. To further the development of such methods, we propose a two-stream model for endoscopic image analysis. Our model fuses two streams of deep feature inputs by mapping their inherent relations through a novel relational network model, to better model symptoms and classify the image. In contrast to handcrafted feature-based models, our proposed network is able to learn features automatically and outperforms existing state-of-the-art methods on two public datasets: KVASIR and Nerthus. Our extensive evaluations illustrate the importance of having two streams of inputs instead of a single stream and also demonstrates the merits of the proposed relational network architecture to combine those streams.

Related papers

• Synthetic Augmentation for Anatomical Landmark Localization using DDPMs [0.22499166814992436]
  diffusion-based generative models have recently started to gain attention for their ability to generate high-quality synthetic images. We propose a novel way to assess the quality of the generated images using a Markov Random Field (MRF) model for landmark matching and a Statistical Shape Model (SSM) to check landmark plausibility.
  arXiv  Detail & Related papers  (2024-10-16T12:09:38Z)
• Real-time guidewire tracking and segmentation in intraoperative x-ray [52.51797358201872]
  We propose a two-stage deep learning framework for real-time guidewire segmentation and tracking. In the first stage, a Yolov5 detector is trained, using the original X-ray images as well as synthetic ones, to output the bounding boxes of possible target guidewires. In the second stage, a novel and efficient network is proposed to segment the guidewire in each detected bounding box.
  arXiv  Detail & Related papers  (2024-04-12T20:39:19Z)
• Unlocking the Heart Using Adaptive Locked Agnostic Networks [4.613517417540153]
  Supervised training of deep learning models for medical imaging applications requires a significant amount of labeled data. To address this limitation, we introduce the Adaptive Locked Agnostic Network (ALAN) ALAN involves self-supervised visual feature extraction using a large backbone model to produce robust semantic self-segmentation. Our findings demonstrate that the self-supervised backbone model robustly identifies anatomical subregions of the heart in an apical four-chamber view.
  arXiv  Detail & Related papers  (2023-09-21T09:06:36Z)
• Two-step interpretable modeling of Intensive Care Acquired Infections [0.0]
  We present a novel methodology for integrating high resolution longitudinal data with the dynamic prediction capabilities of survival models. The aim is two-fold: to improve the predictive power while maintaining interpretability of the models.
  arXiv  Detail & Related papers  (2023-01-26T14:54:17Z)
• MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer [53.575573940055335]
  We propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2. We verify its effectiveness on 20 medical image segmentation tasks with different image modalities.
  arXiv  Detail & Related papers  (2023-01-19T03:42:36Z)
• Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models [1.6352599467675781]
  We propose a method based on diffusion models to detect and segment anomalies in brain imaging. Our diffusion models achieve competitive performance compared with autoregressive approaches across a series of experiments with 2D CT and MRI data.
  arXiv  Detail & Related papers  (2022-06-07T17:30:43Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models [0.0]
  We propose an innovative multi-disease detection pipeline for retinal imaging. Our pipeline includes state-of-the-art strategies like transfer learning, class weighting, real-time image augmentation and Focal loss utilization.
  arXiv  Detail & Related papers  (2021-03-26T18:02:17Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

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.