PathoWAve: A Deep Learning-based Weight Averaging Method for Improving Domain Generalization in Histopathology Images

• URL: http://arxiv.org/abs/2406.15685v1
• Date: Fri, 21 Jun 2024 23:25:44 GMT
• Title: PathoWAve: A Deep Learning-based Weight Averaging Method for Improving Domain Generalization in Histopathology Images
• Authors: Parastoo Sotoudeh Sharifi, M. Omair Ahmad, M. N. S. Swamy,
• Abstract summary: We introduce Pathology Weight Averaging (PathoWAve) to tackle domain shift phenomenon in histopathology image analysis. Our results on Camelyon17 WILDS dataset demonstrate PathoWAve's superiority over previous proposed methods.
• Score: 13.362177469092963
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advancements in deep learning (DL) have significantly advanced medical image analysis. In the field of medical image processing, particularly in histopathology image analysis, the variation in staining protocols and differences in scanners present significant domain shift challenges, undermine the generalization capabilities of models to the data from unseen domains, prompting the need for effective domain generalization (DG) strategies to improve the consistency and reliability of automated cancer detection tools in diagnostic decision-making. In this paper, we introduce Pathology Weight Averaging (PathoWAve), a multi-source DG strategy for addressing domain shift phenomenon of DL models in histopathology image analysis. Integrating specific weight averaging technique with parallel training trajectories and a strategically combination of regular augmentations with histopathology-specific data augmentation methods, PathoWAve enables a comprehensive exploration and precise convergence within the loss landscape. This method significantly enhanced generalization capabilities of DL models across new, unseen histopathology domains. To the best of our knowledge, PathoWAve is the first proposed weight averaging method for DG in histopathology image analysis. Our quantitative results on Camelyon17 WILDS dataset demonstrate PathoWAve's superiority over previous proposed methods to tackle the domain shift phenomenon in histopathology image processing. Our code is available at \url{https://github.com/ParastooSotoudeh/PathoWAve}.

Related papers

• Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment [6.350679043444348]
  Histo-Diffusion is a novel diffusion-based method specially designed for generating and evaluating super-resolution images in digital pathology. It includes a restoration module for histopathology prior and a controllable diffusion module for generating high-quality images.
  arXiv  Detail & Related papers  (2024-08-27T17:31:00Z)
• Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization [0.13108652488669734]
  We propose a novel generative method for domain generalization in histopathology images. Our method employs a generative, self-supervised Vision Transformer to dynamically extract characteristics of image patches. Experiments conducted on two distinct histopathology datasets demonstrate the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2024-07-03T08:20:27Z)
• Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images [53.235117594102675]
  Optical Coherence Tomography Angiography is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. We propose a novel deep-learning framework called Polar-Net to provide interpretable results and leverage clinical prior knowledge. We show that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD.
  arXiv  Detail & Related papers  (2023-11-10T11:49:49Z)
• Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study [9.161480191416551]
  We compare various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. A semi-supervised learning approach is proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images. The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning.
  arXiv  Detail & Related papers  (2023-09-29T13:22:17Z)
• Generalizing Across Domains in Diabetic Retinopathy via Variational Autoencoders [0.0]
  Domain generalization for Diabetic Retinopathy classification allows a model to adeptly classify retinal images. In this study, we explore the inherent capacity of variational autoencoders to disentangle the latent space of fundus images.
  arXiv  Detail & Related papers  (2023-09-20T13:29:22Z)
• Generative Adversarial Networks for Stain Normalisation in Histopathology [2.2166690647926037]
  One of the significant roadblocks to current research is the high level of visual variability across digital pathology images. Sten normalisation aims to standardise the visual profile of digital pathology images without changing the structural content of the images. This is an ongoing field of study as researchers aim to identify a method which efficiently normalises pathology images to make AI models more robust and generalisable.
  arXiv  Detail & Related papers  (2023-08-05T11:38:05Z)
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning [62.25104935889111]
  The training of an efficacious deep learning model requires large data with diverse styles and qualities. A novel contrastive learning is developed to equip the deep learning models with better style generalization capability. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
  arXiv  Detail & Related papers  (2023-04-20T11:40:21Z)
• Self-Supervised Domain Adaptation for Diabetic Retinopathy Grading using Vessel Image Reconstruction [61.58601145792065]
  We learn invariant target-domain features by defining a novel self-supervised task based on retinal vessel image reconstructions. It can be shown that our approach outperforms existing domain strategies.
  arXiv  Detail & Related papers  (2021-07-20T09:44:07Z)
• Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation [62.49837463676111]
  We propose a novel scheme of episodic training with task augmentation on medical imaging classification. Motivated by the limited number of source domains in real-world medical deployment, we consider the unique task-level overfitting.
  arXiv  Detail & Related papers  (2021-06-13T03:56:59Z)
• Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains [68.73614619875814]
  We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Experimental results show that our approach outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains.
  arXiv  Detail & Related papers  (2020-07-04T07:56:02Z)
• 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.