Simple U-net Based Synthetic Polyp Image Generation: Polyp to Negative and Negative to Polyp

• URL: http://arxiv.org/abs/2302.09835v1
• Date: Mon, 20 Feb 2023 08:47:44 GMT
• Title: Simple U-net Based Synthetic Polyp Image Generation: Polyp to Negative and Negative to Polyp
• Authors: Hemin Ali Qadir, Ilangko Balasingham, Younghak Shin
• Abstract summary: Synthetic polyp generation is a good alternative to overcome the privacy problem of medical data. We propose a deep learning-based polyp image generation framework that generates synthetic polyp images similar to real ones.
• Score: 5.943584566266871
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Synthetic polyp generation is a good alternative to overcome the privacy problem of medical data and the lack of various polyp samples. In this study, we propose a deep learning-based polyp image generation framework that generates synthetic polyp images that are similar to real ones. We suggest a framework that converts a given polyp image into a negative image (image without a polyp) using a simple conditional GAN architecture and then converts the negative image into a new-looking polyp image using the same network. In addition, by using the controllable polyp masks, polyps with various characteristics can be generated from one input condition. The generated polyp images can be used directly as training images for polyp detection and segmentation without additional labeling. To quantitatively assess the quality of generated synthetic polyps, we use public polyp image and video datasets combined with the generated synthetic images to examine the performance improvement of several detection and segmentation models. Experimental results show that we obtain performance gains when the generated polyp images are added to the training set.

Related papers

• EndoFinder: Online Image Retrieval for Explainable Colorectal Polyp Diagnosis [10.83700068295662]
  EndoFinder is a content-based image retrieval framework. It finds the 'digital twin' polyp in the reference database given a newly detected polyp. The clinical semantics of the new polyp can be inferred referring to the matched ones.
  arXiv  Detail & Related papers  (2024-07-16T05:40:17Z)
• Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation [0.0]
  Polyp-DDPM is a diffusion-based method for generating realistic images of polyps conditioned on masks. Our approach addresses the challenges of data limitations, high annotation costs, and privacy concerns associated with medical images.
  arXiv  Detail & Related papers  (2024-02-06T14:26:02Z)
• Mask-conditioned latent diffusion for generating gastrointestinal polyp images [2.027538200191349]
  This study proposes a conditional DPM framework to generate synthetic GI polyp images conditioned on given segmentation masks. Our system can generate an unlimited number of high-fidelity synthetic polyp images with the corresponding ground truth masks of polyps. Results show that the best micro-imagewise IOU of 0.7751 was achieved from DeepLabv3+ when the training data consists of both real data and synthetic data.
  arXiv  Detail & Related papers  (2023-04-11T14:11:17Z)
• 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)
• PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps [1.7915968197912802]
  Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. CAD systems to detect polyps can improve detection accuracy and efficiency and save the time of the domain experts called endoscopists. We propose the PolypConnect pipeline, which can convert non-polyp images into polyp images to increase the size of training datasets for training.
  arXiv  Detail & Related papers  (2022-05-30T20:20:19Z)
• Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection [30.51410140271929]
  Current polyp detection methods from colonoscopy videos use exclusively normal (i.e., healthy) training images. We formulate polyp detection as a weakly-supervised anomaly detection task that uses video-level labelled training data to detect frame-level polyps.
  arXiv  Detail & Related papers  (2022-03-23T01:30:48Z)
• Automatic Polyp Segmentation via Multi-scale Subtraction Network [100.94922587360871]
  In clinical practice, precise polyp segmentation provides important information in the early detection of colorectal cancer. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder. We propose a multi-scale subtraction network (MSNet) to segment polyp from colonoscopy image.
  arXiv  Detail & Related papers  (2021-08-11T07:54:07Z)
• Few-Shot Domain Adaptation with Polymorphic Transformers [50.128636842853155]
  Deep neural networks (DNNs) trained on one set of medical images often experience severe performance drop on unseen test images. Few-shot domain adaptation, i.e., adapting a trained model with a handful of annotations, is highly practical and useful in this case. We propose a Polymorphic Transformer (Polyformer) which can be incorporated into any DNN backbone for few-shot domain adaptation.
  arXiv  Detail & Related papers  (2021-07-10T10:08:57Z)
• CoPE: Conditional image generation using Polynomial Expansions [50.67390290190874]
  We introduce a general framework, called CoPE, that enables an expansion of two input variables and captures their auto- and cross-correlations. CoPE is evaluated in five tasks (class-generation, inverse problems, edges-to-image translation, image-to-image translation, attribute-guided generation) involving eight datasets. The thorough evaluation suggests that CoPE can be useful for tackling diverse conditional generation tasks.
  arXiv  Detail & Related papers  (2021-04-11T19:02:37Z)
• Colonoscopy Polyp Detection: Domain Adaptation From Medical Report Images to Real-time Videos [76.37907640271806]
  We propose an Image-video-joint polyp detection network (Ivy-Net) to address the domain gap between colonoscopy images from historical medical reports and real-time videos. Experiments on the collected dataset demonstrate that our Ivy-Net achieves the state-of-the-art result on colonoscopy video.
  arXiv  Detail & Related papers  (2020-12-31T10:33:09Z)

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.