Conditioned Generative Transformers for Histopathology Image Synthetic Augmentation

• URL: http://arxiv.org/abs/2212.09977v1
• Date: Tue, 20 Dec 2022 03:40:44 GMT
• Title: Conditioned Generative Transformers for Histopathology Image Synthetic Augmentation
• Authors: Meng Li, Chaoyi Li, Can Peng, Brian Lovell
• Abstract summary: Vision transformer (ViT) based generative adversarial networks (GANs) recently demonstrated superior potential in general image synthesis. We propose a pure ViT-based conditional GAN model for histopathology image synthetic augmentation.
• Score: 3.1616973611119494
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep learning networks have demonstrated state-of-the-art performance on medical image analysis tasks. However, the majority of the works rely heavily on abundantly labeled data, which necessitates extensive involvement of domain experts. Vision transformer (ViT) based generative adversarial networks (GANs) recently demonstrated superior potential in general image synthesis, yet are less explored for histopathology images. In this paper, we address these challenges by proposing a pure ViT-based conditional GAN model for histopathology image synthetic augmentation. To alleviate training instability and improve generation robustness, we first introduce a conditioned class projection method to facilitate class separation. We then implement a multi-loss weighing function to dynamically balance the losses between classification tasks. We further propose a selective augmentation mechanism to actively choose the appropriate generated images and bring additional performance improvements. Extensive experiments on the histopathology datasets show that leveraging our synthetic augmentation framework results in significant and consistent improvements in classification performance.

Related papers

• Feature-to-Image Data Augmentation: Improving Model Feature Extraction with Cluster-Guided Synthetic Samples [4.041834517339835]
  This study introduces FICAug, a novel feature-to-image data augmentation framework. It is designed to improve model generalization under limited data conditions by generating structured synthetic samples. Experimental results demonstrate that FICAug significantly improves classification accuracy.
  arXiv  Detail & Related papers  (2024-09-26T09:51:08Z)
• Unleashing the Potential of Synthetic Images: A Study on Histopathology Image Classification [0.12499537119440242]
  Histopathology image classification is crucial for the accurate identification and diagnosis of various diseases. We show that synthetic images can effectively augment existing datasets, ultimately improving the performance of the downstream histopathology image classification task.
  arXiv  Detail & Related papers  (2024-09-24T12:02:55Z)
• Dataset Distillation for Histopathology Image Classification [46.04496989951066]
  We introduce a novel dataset distillation algorithm tailored for histopathology image datasets (Histo-DD) We conduct a comprehensive evaluation of the effectiveness of the proposed algorithm and the generated histopathology samples in both patch-level and slide-level classification tasks.
  arXiv  Detail & Related papers  (2024-08-19T05:53:38Z)
• Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attacks [5.0243930429558885]
  This paper introduces Knowledge Recycling (KR), a pipeline designed to optimise the generation and use of synthetic data for training downstream classifiers. At the heart of this pipeline is Generative Knowledge Distillation (GKD), the proposed technique that significantly improves the quality and usefulness of the information. The results show a significant reduction in the performance gap between models trained on real and synthetic data, with models based on synthetic data outperforming those trained on real data in some cases.
  arXiv  Detail & Related papers  (2024-07-22T10:31:07Z)
• TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification [0.011037620731410175]
  This work aims to guide the generative model to synthesize data with high uncertainty. We alter the feature space of the autoencoder through an optimization process. We improve the robustness against test time data augmentations and adversarial attacks on several classifications tasks.
  arXiv  Detail & Related papers  (2024-06-25T11:38:46Z)
• Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization [62.157627519792946]
  We introduce a novel framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model to improve its transferability. We propose dataset style inversion strategy to improve the stylistic alignment between synthetic and real images. Our proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements.
  arXiv  Detail & Related papers  (2024-03-28T22:25:05Z)
• Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification [57.1795052451257]
  We study the dependence of the GAN-based augmentation performance on dataset size with a focus on small samples. We train StyleGAN2-ADA with both sets and then, after validating the quality of generated images, we use trained GANs as one of the augmentations approaches in multi-class classification problems. The GAN-based augmentation approach is found to be comparable with classical augmentation in the case of medium and large datasets but underperforms in the case of smaller datasets.
  arXiv  Detail & Related papers  (2024-01-26T08:28:13Z)
• Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning [20.175586324567025]
  Mitigating catastrophic forgetting is a key hurdle in continual learning. A major issue is the deterioration in the quality of generated data compared to the original. We propose a GR-based approach for continual learning that enhances image quality in generators.
  arXiv  Detail & Related papers  (2023-12-10T17:39:42Z)
• Performance of GAN-based augmentation for deep learning COVID-19 image classification [57.1795052451257]
  The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
  arXiv  Detail & Related papers  (2023-04-18T15:39:58Z)
• IRGen: Generative Modeling for Image Retrieval [82.62022344988993]
  In this paper, we present a novel methodology, reframing image retrieval as a variant of generative modeling. We develop our model, dubbed IRGen, to address the technical challenge of converting an image into a concise sequence of semantic units. Our model achieves state-of-the-art performance on three widely-used image retrieval benchmarks and two million-scale datasets.
  arXiv  Detail & Related papers  (2023-03-17T17:07:36Z)
• Is synthetic data from generative models ready for image recognition? [69.42645602062024]
  We study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks.
  arXiv  Detail & Related papers  (2022-10-14T06:54:24Z)
• High-Fidelity Synthesis with Disentangled Representation [60.19657080953252]
  We propose an Information-Distillation Generative Adrial Network (ID-GAN) for disentanglement learning and high-fidelity synthesis. Our method learns disentangled representation using VAE-based models, and distills the learned representation with an additional nuisance variable to the separate GAN-based generator for high-fidelity synthesis. Despite the simplicity, we show that the proposed method is highly effective, achieving comparable image generation quality to the state-of-the-art methods using the disentangled representation.
  arXiv  Detail & Related papers  (2020-01-13T14:39:40Z)

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.