PHG-Net: Persistent Homology Guided Medical Image Classification

• URL: http://arxiv.org/abs/2311.17243v1
• Date: Tue, 28 Nov 2023 21:34:06 GMT
• Title: PHG-Net: Persistent Homology Guided Medical Image Classification
• Authors: Yaopeng Peng, Hongxiao Wang, Milan Sonka and Danny Z. Chen
• Abstract summary: We propose a persistent homology guided approach (PHG-Net) that explores topological features of objects for medical image classification. For an input image, we first compute its cubical persistence diagram and extract topological features into a vector representation. The extracted topological features are then incorporated into the feature map generated by CNN or Transformer for feature fusion.
• Score: 14.450329809640422
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern deep neural networks have achieved great successes in medical image analysis. However, the features captured by convolutional neural networks (CNNs) or Transformers tend to be optimized for pixel intensities and neglect key anatomical structures such as connected components and loops. In this paper, we propose a persistent homology guided approach (PHG-Net) that explores topological features of objects for medical image classification. For an input image, we first compute its cubical persistence diagram and extract topological features into a vector representation using a small neural network (called the PH module). The extracted topological features are then incorporated into the feature map generated by CNN or Transformer for feature fusion. The PH module is lightweight and capable of integrating topological features into any CNN or Transformer architectures in an end-to-end fashion. We evaluate our PHG-Net on three public datasets and demonstrate its considerable improvements on the target classification tasks over state-of-the-art methods.

Related papers

• Image Classification using Combination of Topological Features and Neural Networks [1.0323063834827417]
  We use the persistent homology method, a technique in topological data analysis (TDA), to extract essential topological features from the data space. This was carried out with the aim of classifying images from multiple classes in the MNIST dataset. Our approach inserts topological features into deep learning approaches composed by single and two-streams neural networks.
  arXiv  Detail & Related papers  (2023-11-10T20:05:40Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Unleashing the Power of Depth and Pose Estimation Neural Networks by Designing Compatible Endoscopic Images [12.412060445862842]
  We conduct a detail analysis of the properties of endoscopic images and improve the compatibility of images and neural networks. First, we introcude the Mask Image Modelling (MIM) module, which inputs partial image information instead of complete image information. Second, we propose a lightweight neural network to enhance the endoscopic images, to explicitly improve the compatibility between images and neural networks.
  arXiv  Detail & Related papers  (2023-09-14T02:19:38Z)
• NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentation [3.8336080345323227]
  CNN and Transformer variants have emerged as the leading medical image segmentation backbones. We propose NexToU, a novel hybrid architecture for medical image segmentation. Our method consistently outperforms other state-of-the-art (SOTA) architectures.
  arXiv  Detail & Related papers  (2023-05-25T10:18:57Z)
• Learnable Filters for Geometric Scattering Modules [64.03877398967282]
  We propose a new graph neural network (GNN) module based on relaxations of recently proposed geometric scattering transforms. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations.
  arXiv  Detail & Related papers  (2022-08-15T22:30:07Z)
• How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images [2.2699159408903484]
  We propose a fusion framework for enhancing the global image-level representation captured by convolutional neural networks (CNNs) We evaluate our fusion strategies on histology datasets curated from large patient cohorts of colorectal and gastric cancers.
  arXiv  Detail & Related papers  (2022-06-15T15:27:48Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentation [7.140322699310487]
  We propose a novel hybrid architecture for medical image segmentation called PHTrans. PHTrans parallelly hybridizes Transformer and CNN in main building blocks to produce hierarchical representations from global and local features.
  arXiv  Detail & Related papers  (2022-03-09T08:06:56Z)
• Topological obstructions in neural networks learning [67.8848058842671]
  We study global properties of the loss gradient function flow. We use topological data analysis of the loss function and its Morse complex to relate local behavior along gradient trajectories with global properties of the loss surface.
  arXiv  Detail & Related papers  (2020-12-31T18:53:25Z)
• Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition [126.51241919472356]
  We design a simple and highly modularized graph convolutional network architecture for skeleton-based action recognition. Our network is constructed by repeating a building block that aggregates multi-granularity information from both the spatial and temporal paths.
  arXiv  Detail & Related papers  (2020-11-26T14:43:04Z)
• Data-Driven Learning of Geometric Scattering Networks [74.3283600072357]
  We propose a new graph neural network (GNN) module based on relaxations of recently proposed geometric scattering transforms. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations.
  arXiv  Detail & Related papers  (2020-10-06T01:20:27Z)

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.