Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation Learning

• URL: http://arxiv.org/abs/2412.10482v1
• Date: Fri, 13 Dec 2024 10:18:36 GMT
• Title: Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation Learning
• Authors: Zhenfeng Zhuang, Min Cen, Yanfeng Li, Fangyu Zhou, Lequan Yu, Baptiste Magnier, Liansheng Wang,
• Abstract summary: We introduce H-MGDM, a novel self-supervised Histopathology image representation learning method through the Dynamic Entity-Masked Graph Diffusion Model. Specifically, we propose to use complementary subgraphs as latent diffusion conditions and self-supervised targets respectively during pre-training.
• Score: 25.197342542821843
• License:
• Abstract: Significant disparities between the features of natural images and those inherent to histopathological images make it challenging to directly apply and transfer pre-trained models from natural images to histopathology tasks. Moreover, the frequent lack of annotations in histopathology patch images has driven researchers to explore self-supervised learning methods like mask reconstruction for learning representations from large amounts of unlabeled data. Crucially, previous mask-based efforts in self-supervised learning have often overlooked the spatial interactions among entities, which are essential for constructing accurate representations of pathological entities. To address these challenges, constructing graphs of entities is a promising approach. In addition, the diffusion reconstruction strategy has recently shown superior performance through its random intensity noise addition technique to enhance the robust learned representation. Therefore, we introduce H-MGDM, a novel self-supervised Histopathology image representation learning method through the Dynamic Entity-Masked Graph Diffusion Model. Specifically, we propose to use complementary subgraphs as latent diffusion conditions and self-supervised targets respectively during pre-training. We note that the graph can embed entities' topological relationships and enhance representation. Dynamic conditions and targets can improve pathological fine reconstruction. Our model has conducted pretraining experiments on three large histopathological datasets. The advanced predictive performance and interpretability of H-MGDM are clearly evaluated on comprehensive downstream tasks such as classification and survival analysis on six datasets. Our code will be publicly available at https://github.com/centurion-crawler/H-MGDM.

Related papers

• Unmasking unlearnable models: a classification challenge for biomedical images without visible cues [0.0]
  We demystify the complexity of MGMT status prediction through a comprehensive exploration. Our finding highlighted that current models are unlearnable and may require new architectures to explore applications in the real world.
  arXiv  Detail & Related papers  (2024-07-29T08:12:42Z)
• Mask-guided cross-image attention for zero-shot in-silico histopathologic image generation with a diffusion model [0.10910416614141322]
  Diffusion models are the state-of-the-art solution for generating in-silico images. Appearance transfer diffusion models are designed for natural images. In computational pathology, specifically in oncology, it is not straightforward to define which objects in an image should be classified as foreground and background. We contribute to the applicability of appearance transfer models to diffusion-stained images by modifying the appearance transfer guidance to alternate between class-specific AdaIN feature statistics matchings.
  arXiv  Detail & Related papers  (2024-07-16T12:36:26Z)
• Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain [18.165807360855435]
  Graph Neural Networks (GNNs) have shown promise in learning dynamic functional connectivity for distinguishing phenotypes from human brain networks. We introduce the Spatio-Temporal Joint Embedding Masked Autoencoder (ST-JEMA), drawing inspiration from the Joint Embedding Predictive Architecture (JEPA) in computer vision.
  arXiv  Detail & Related papers  (2024-03-11T04:49:41Z)
• Learned representation-guided diffusion models for large-image generation [58.192263311786824]
  We introduce a novel approach that trains diffusion models conditioned on embeddings from self-supervised learning (SSL) Our diffusion models successfully project these features back to high-quality histopathology and remote sensing images. Augmenting real data by generating variations of real images improves downstream accuracy for patch-level and larger, image-scale classification tasks.
  arXiv  Detail & Related papers  (2023-12-12T14:45:45Z)
• Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks [64.67735676127208]
  Text-to-image diffusion models have shown great potential for benefiting image recognition. Although promising, there has been inadequate exploration dedicated to unsupervised learning on diffusion-generated images. We introduce customized solutions by fully exploiting the aforementioned free attention masks.
  arXiv  Detail & Related papers  (2023-08-13T10:07:46Z)
• Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis [48.02011627390706]
  We develop a novel self-supervised approach that occludes targeted regions to improve the pre-training procedure. We show that training the agent against the prediction model can significantly improve the semantic features extracted for downstream classification tasks.
  arXiv  Detail & Related papers  (2022-03-25T19:05:06Z)
• Data-driven generation of plausible tissue geometries for realistic photoacoustic image synthesis [53.65837038435433]
  Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties. We propose a novel approach to PAT data simulation, which we refer to as "learning to simulate" We leverage the concept of Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data to generate plausible tissue geometries.
  arXiv  Detail & Related papers  (2021-03-29T11:30:18Z)
• 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)
• Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples [52.549928980694695]
  In situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared. labeling training data with precise stages is very time-consuming even for biologists. We propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images.
  arXiv  Detail & Related papers  (2020-10-20T06:06:06Z)
• Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics [22.04114134677181]
  We present a novel method for the unsupervised domain adaptation for histological image analysis. It is based on a backbone for embedding images into a feature space, and a graph neural layer for propa-gating the supervision signals of images with labels. In experiments, our methodachieves state-of-the-art performance on four public datasets.
  arXiv  Detail & Related papers  (2020-08-21T04:53:44Z)

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.