PathoPainter: Augmenting Histopathology Segmentation via Tumor-aware Inpainting

• URL: http://arxiv.org/abs/2503.04634v1
• Date: Thu, 06 Mar 2025 17:21:12 GMT
• Title: PathoPainter: Augmenting Histopathology Segmentation via Tumor-aware Inpainting
• Authors: Hong Liu, Haosen Yang, Evi M. C. Huijben, Mark Schuiveling, Ruisheng Su, Josien P. W. Pluim, Mitko Veta,
• Abstract summary: We propose PathoPainter, which reformulates image-mask pair generation as a tumor inpainting task.<n>Our approach preserves the background while inpainting the tumor region, ensuring precise alignment between the generated image and its corresponding mask.<n>Our comprehensive evaluation spans multiple datasets featuring diverse tumor types and various training data scales.
• Score: 7.518548705907955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tumor segmentation plays a critical role in histopathology, but it requires costly, fine-grained image-mask pairs annotated by pathologists. Thus, synthesizing histopathology data to expand the dataset is highly desirable. Previous works suffer from inaccuracies and limited diversity in image-mask pairs, both of which affect training segmentation, particularly in small-scale datasets and the inherently complex nature of histopathology images. To address this challenge, we propose PathoPainter, which reformulates image-mask pair generation as a tumor inpainting task. Specifically, our approach preserves the background while inpainting the tumor region, ensuring precise alignment between the generated image and its corresponding mask. To enhance dataset diversity while maintaining biological plausibility, we incorporate a sampling mechanism that conditions tumor inpainting on regional embeddings from a different image. Additionally, we introduce a filtering strategy to exclude uncertain synthetic regions, further improving the quality of the generated data. Our comprehensive evaluation spans multiple datasets featuring diverse tumor types and various training data scales. As a result, segmentation improved significantly with our synthetic data, surpassing existing segmentation data synthesis approaches, e.g., 75.69% -> 77.69% on CAMELYON16. The code is available at https://github.com/HongLiuuuuu/PathoPainter.

Related papers

• PathSegDiff: Pathology Segmentation using Diffusion model representations [63.20694440934692]
  We propose PathSegDiff, a novel approach for histopathology image segmentation that leverages Latent Diffusion Models (LDMs) as pre-trained featured extractors. Our method utilizes a pathology-specific LDM, guided by a self-supervised encoder, to extract rich semantic information from H&E stained histopathology images. Our experiments demonstrate significant improvements over traditional methods on the BCSS and GlaS datasets.
  arXiv  Detail & Related papers  (2025-04-09T14:58:21Z)
• Enhanced MRI Representation via Cross-series Masking [48.09478307927716]
  Cross-Series Masking (CSM) Strategy for effectively learning MRI representation in a self-supervised manner.<n>Method achieves state-of-the-art performance on both public and in-house datasets.
  arXiv  Detail & Related papers  (2024-12-10T10:32:09Z)
• 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)
• Diffusion-based Data Augmentation for Nuclei Image Segmentation [68.28350341833526]
  We introduce the first diffusion-based augmentation method for nuclei segmentation. The idea is to synthesize a large number of labeled images to facilitate training the segmentation model. The experimental results show that by augmenting 10% labeled real dataset with synthetic samples, one can achieve comparable segmentation results.
  arXiv  Detail & Related papers  (2023-10-22T06:16:16Z)
• 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)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images [10.832913704956253]
  Developing an AI-assisted gland segmentation method from histology images is critical for automatic cancer diagnosis and prognosis. Existing weakly-supervised semantic segmentation methods in computer vision achieve degenerative results for gland segmentation. We propose a novel method Online Easy Example Mining (OEEM) that encourages the network to focus on credible supervision signals.
  arXiv  Detail & Related papers  (2022-06-14T07:53:03Z)
• Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification [11.087537668968888]
  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.
  arXiv  Detail & Related papers  (2021-11-10T23:25:39Z)
• 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)
• METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy [4.872960046536882]
  We introduce a novel generative method which leverages real anatomical information to generate realistic image-label pairs of tumours. We construct a dual-pathway generator, for the anatomical image and label, trained in a cycle-consistent setup, constrained by an independent, pretrained segmentor. The generated images yield significant quantitative improvement compared to existing methods.
  arXiv  Detail & Related papers  (2021-04-22T11:18:17Z)
• Generative Synthetic Augmentation using Label-to-Image Translation for Nuclei Image Segmentation [0.0]
  We propose a synthetic augmentation using label-to-image translation, mapping from a semantic label with the edge structure to a real image. We compute and report that a proposed synthetic augmentation procedure improve their accuracy.
  arXiv  Detail & Related papers  (2020-04-21T16:10:11Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.