Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification

• URL: http://arxiv.org/abs/2111.06399v1
• Date: Wed, 10 Nov 2021 23:25:39 GMT
• Title: Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification
• Authors: Yuan Xue, Jiarong Ye, Qianying Zhou, Rodney Long, Sameer Antani, Zhiyun Xue, Carl Cornwell, Richard Zaino, Keith Cheng, Xiaolei Huang
• Abstract summary: We propose a conditional GAN model, namely HistoGAN, for synthesizing realistic histopathology image patches conditioned on class labels. We also investigate a novel synthetic augmentation framework that selectively adds new synthetic image patches generated by our proposed HistoGAN. Our models are evaluated on two datasets: a cervical histopathology image dataset with limited annotations, and another dataset of lymph node histopathology images with metastatic cancer.
• Score: 11.087537668968888
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Histopathological analysis is the present gold standard for precancerous lesion diagnosis. The goal of automated histopathological classification from digital images requires supervised training, which requires a large number of expert annotations that can be expensive and time-consuming to collect. Meanwhile, accurate classification of image patches cropped from whole-slide images is essential for standard sliding window based histopathology slide classification methods. To mitigate these issues, we propose a carefully designed conditional GAN model, namely HistoGAN, for synthesizing realistic histopathology image patches conditioned on class labels. We also investigate a novel synthetic augmentation framework that selectively adds new synthetic image patches generated by our proposed HistoGAN, rather than expanding directly the training set with synthetic images. By selecting synthetic images based on the confidence of their assigned labels and their feature similarity to real labeled images, our framework provides quality assurance to synthetic augmentation. Our models are evaluated on two datasets: a cervical histopathology image dataset with limited annotations, and another dataset of lymph node histopathology images with metastatic cancer. Here, we show that leveraging HistoGAN generated images with selective augmentation results in significant and consistent improvements of classification performance (6.7% and 2.8% higher accuracy, respectively) for cervical histopathology and metastatic cancer datasets.

Related papers

• 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)
• Two-stage Cytopathological Image Synthesis for Augmenting Cervical Abnormality Screening [13.569003698448]
  Pathological image synthesis is naturally raised to minimize the efforts in data collection and annotation. We propose a two-stage image synthesis framework to create synthetic data for augmenting cervical abnormality screening. Our experiments demonstrate the synthetic image quality, diversity, and controllability of the proposed synthesis framework.
  arXiv  Detail & Related papers  (2024-02-22T17:06:47Z)
• 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)
• Enhanced Sharp-GAN For Histopathology Image Synthesis [63.845552349914186]
  Histopathology image synthesis aims to address the data shortage issue in training deep learning approaches for accurate cancer detection. We propose a novel approach that enhances the quality of synthetic images by using nuclei topology and contour regularization. The proposed approach outperforms Sharp-GAN in all four image quality metrics on two datasets.
  arXiv  Detail & Related papers  (2023-01-24T17:54:01Z)
• Unified Framework for Histopathology Image Augmentation and Classification via Generative Models [6.404713841079193]
  We propose an innovative unified framework that integrates the data generation and model training stages into a unified process. Our approach utilizes a pure Vision Transformer (ViT)-based conditional Generative Adversarial Network (cGAN) model to simultaneously handle both image synthesis and classification. Our experiments show that our unified synthetic augmentation framework consistently enhances the performance of histopathology image classification models.
  arXiv  Detail & Related papers  (2022-12-20T03:40:44Z)
• Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders [50.689585476660554]
  We propose a new fine-tuning strategy that includes positive-pair loss relaxation and random sentence sampling. Our approach consistently improves overall zero-shot pathology classification across four chest X-ray datasets and three pre-trained models.
  arXiv  Detail & Related papers  (2022-12-14T06:04:18Z)
• Deepfake histological images for enhancing digital pathology [0.40631409309544836]
  We develop a generative adversarial network model that synthesizes pathology images constrained by class labels. We investigate the ability of this framework in synthesizing realistic prostate and colon tissue images. We extend the approach to significantly more complex images from colon biopsies and show that the complex microenvironment in such tissues can also be reproduced.
  arXiv  Detail & Related papers  (2022-06-16T17:11:08Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis [65.47507533905188]
  Conditional generative adversarial networks have been applied to generate synthetic histopathology images. We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images.
  arXiv  Detail & Related papers  (2021-10-27T18:54:25Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Synthetic Sample Selection via Reinforcement Learning [8.099072894865802]
  We propose a reinforcement learning based synthetic sample selection method that learns to choose synthetic images containing reliable and informative features. In experiments on a cervical dataset and a lymph node dataset, the image classification performance is improved by 8.1% and 2.3%, respectively.
  arXiv  Detail & Related papers  (2020-08-26T01:34:19Z)

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.