Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation

• URL: http://arxiv.org/abs/2106.01915v1
• Date: Thu, 3 Jun 2021 15:08:14 GMT
• Title: Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation
• Authors: Changhee Han
• Abstract summary: Generative Adversarial Networks (GANs) can generate realistic but novel samples, and thus effectively cover the real image distribution. This thesis contains four GAN projects aiming to present such novel applications' clinical relevance in collaboration with physicians.
• Score: 0.22843885788439805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional Neural Networks (CNNs) can play a key role in Medical Image Analysis under large-scale annotated datasets. However, preparing such massive dataset is demanding. In this context, Generative Adversarial Networks (GANs) can generate realistic but novel samples, and thus effectively cover the real image distribution. In terms of interpolation, the GAN-based medical image augmentation is reliable because medical modalities can display the human body's strong anatomical consistency at fixed position while clearly reflecting inter-subject variability; thus, we propose to use noise-to-image GANs (e.g., random noise samples to diverse pathological images) for (i) medical Data Augmentation (DA) and (ii) physician training. Regarding the DA, the GAN-generated images can improve Computer-Aided Diagnosis based on supervised learning. For the physician training, the GANs can display novel desired pathological images and help train medical trainees despite infrastructural/legal constraints. This thesis contains four GAN projects aiming to present such novel applications' clinical relevance in collaboration with physicians. Whereas the methods are more generally applicable, this thesis only explores a few oncological applications.

Related papers

• Medical Imaging Complexity and its Effects on GAN Performance [1.776717121506676]
  Medical image synthesis via generative adversarial networks (GANs) emerged as a powerful method for synthetically generating photo-realistic images. We experimentally establish benchmarks that measure the relationship between a sample dataset size and the fidelity of the generated images. We conduct experiments with two state-of-the-art GANs, StyleGAN 3 and SPADE-GAN, trained on multiple medical imaging datasets with variable sample sizes.
  arXiv  Detail & Related papers  (2024-10-23T15:28:25Z)
• Applying Conditional Generative Adversarial Networks for Imaging Diagnosis [3.881664394416534]
  This study introduces an innovative application of Conditional Generative Adversarial Networks (C-GAN) integrated with Stacked Hourglass Networks (SHGN) We address the problem of overfitting, common in deep learning models applied to complex imaging datasets, by augmenting data through rotation and scaling. A hybrid loss function combining L1 and L2 reconstruction losses, enriched with adversarial training, is introduced to refine segmentation processes in intravascular ultrasound (IVUS) imaging.
  arXiv  Detail & Related papers  (2024-07-17T23:23:09Z)
• A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images [0.5999777817331317]
  We introduce a framework to create echocardiography images suitable to be used for clinical research purposes. For several domain translation operations, the results verified that such generative model was able to synthesize high quality image samples.
  arXiv  Detail & Related papers  (2024-03-07T15:58:03Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• GANs for Medical Image Synthesis: An Empirical Study [12.36854197042851]
  Generative Adversarial Networks (GANs) have become increasingly powerful, generating mind-blowing photorealistic images. In this paper, we perform a multi-GAN and multi-application study to gauge the benefits of GANs in medical imaging.
  arXiv  Detail & Related papers  (2021-05-11T19:21:39Z)
• 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)
• Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
  The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. In this paper, we develop a novel generative method named generative adversarial U-Net. Our newly designed model is domain-free and generalizable to various medical images.
  arXiv  Detail & Related papers  (2021-01-12T23:02:26Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization [59.5104563755095]
  We introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding.
  arXiv  Detail & Related papers  (2020-09-27T12:30:30Z)
• Medical Image Generation using Generative Adversarial Networks [0.0]
  Generative adversarial networks (GANs) are unsupervised Deep Learning approach in the computer vision community. GANs generate realistic medical images and corresponding annotations. The various framework of GANs which gained popularity in the interpretation of medical images, such as Deep Convolutional GAN (DCGAN), Laplacian GAN (LAPGAN), pix2pix, CycleGAN, and unsupervised image-to-image translation model (UNIT)
  arXiv  Detail & Related papers  (2020-05-19T20:31:57Z)

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.