SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation

• URL: http://arxiv.org/abs/2103.03604v1
• Date: Fri, 5 Mar 2021 11:12:22 GMT
• Title: SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation
• Authors: Boxiang Yun, Yan Wang, Jieneng Chen, Huiyu Wang, Wei Shen, Qingli Li
• Abstract summary: We name our method Spectral Transformer (SpecTr), which has a strong ability to model long-range dependency among spectral bands. SpecTr outperforms other competing methods in a hyperspectral pathology image segmentation benchmark without the need of pre-training.
• Score: 14.34998033157658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of applications relied on high-precision pathology image segmentation, such as computational pathology and precision medicine. Since hyperspectral pathology images benefit from the rich and detailed spectral information even beyond the visible spectrum, the key to achieve high-precision hyperspectral pathology image segmentation is to felicitously model the context along high-dimensional spectral bands. Inspired by the strong context modeling ability of transformers, we hereby, for the first time, formulate the contextual feature learning across spectral bands for hyperspectral pathology image segmentation as a sequence-to-sequence prediction procedure by transformers. To assist spectral context learning procedure, we introduce two important strategies: (1) a sparsity scheme enforces the learned contextual relationship to be sparse, so as to eliminates the distraction from the redundant bands; (2) a spectral normalization, a separate group normalization for each spectral band, mitigates the nuisance caused by heterogeneous underlying distributions of bands. We name our method Spectral Transformer (SpecTr), which enjoys two benefits: (1) it has a strong ability to model long-range dependency among spectral bands, and (2) it jointly explores the spatial-spectral features of HSI. Experiments show that SpecTr outperforms other competing methods in a hyperspectral pathology image segmentation benchmark without the need of pre-training. Code is available at https://github.com/hfut-xc-yun/SpecTr.

Related papers

• Deep Spectral Methods for Unsupervised Ultrasound Image Interpretation [53.37499744840018]
  This paper proposes a novel unsupervised deep learning strategy tailored to ultrasound to obtain easily interpretable tissue separations. We integrate key concepts from unsupervised deep spectral methods, which combine spectral graph theory with deep learning methods. We utilize self-supervised transformer features for spectral clustering to generate meaningful segments based on ultrasound-specific metrics and shape and positional priors, ensuring semantic consistency across the dataset.
  arXiv  Detail & Related papers  (2024-08-04T14:30:14Z)
• Correlation Hyperspectral Imaging [0.0]
  Hyperspectral imaging aims at providing information on both the spatial and the spectral distribution of light, with high resolution. We address this limitation by exploiting light intensity correlations, which are shown to enable overcoming the typical downsides of traditional hyperspectral imaging techniques. The enabled combination of high spatial and spectral resolution, high speed, and insensitivity to undesired spectral features shall lead to a paradigm change in hyperspectral imaging devices.
  arXiv  Detail & Related papers  (2024-07-18T19:54:44Z)
• Spectral Graph Reasoning Network for Hyperspectral Image Classification [0.43512163406551996]
  Convolutional neural networks (CNNs) have achieved remarkable performance in hyperspectral image (HSI) classification. We propose a spectral graph reasoning network (SGR) learning framework comprising two crucial modules. Experiments on two HSI datasets demonstrate that the proposed architecture can significantly improve the classification accuracy.
  arXiv  Detail & Related papers  (2024-07-02T20:29:23Z)
• Spectral-wise Implicit Neural Representation for Hyperspectral Image Reconstruction [14.621504062838731]
  Coded Aperture Snapshot Spectral Imaging (CASSI) reconstruction aims to recover the 3D spatial-spectral signal from 2D measurement. Existing methods for reconstructing HSI typically involve learning mappings from a 2D compressed image to a predetermined set of discrete spectral bands. We propose an innovative method called Spectral-wise Implicit Neural Representation (SINR) as a pioneering step toward addressing this limitation.
  arXiv  Detail & Related papers  (2023-12-02T08:06:07Z)
• SpectralGPT: Spectral Remote Sensing Foundation Model [60.023956954916414]
  A universal RS foundation model, named SpectralGPT, is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT) Compared to existing foundation models, SpectralGPT accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS big data. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS big data applications within the field of geoscience.
  arXiv  Detail & Related papers  (2023-11-13T07:09:30Z)
• DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification [6.521187080027966]
  We propose a new network called DiffSpectralNet, which combines diffusion and transformer techniques. First, we use an unsupervised learning framework based on the diffusion model to extract both high-level and low-level spectral-spatial features. The diffusion method is capable of extracting diverse and meaningful spectral-spatial features, leading to improvement in HSI classification.
  arXiv  Detail & Related papers  (2023-10-29T15:26:37Z)
• Boosting the Generalization Ability for Hyperspectral Image Classification using Spectral-spatial Axial Aggregation Transformer [14.594398447576188]
  In the hyperspectral image classification (HSIC) task, the most commonly used model validation paradigm is partitioning the training-test dataset through pixel-wise random sampling. In our experiments, we found that the high accuracy was reached because the training and test datasets share a lot of information. We propose a spectral-spatial axial aggregation transformer model, namely SaaFormer, that preserves generalization across dataset partitions.
  arXiv  Detail & Related papers  (2023-06-29T07:55:43Z)
• Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution [82.59267937569213]
  High-resolution (HR) hyperspectral face image plays an important role in face related computer vision tasks under uncontrolled conditions. In this paper, we investigate how to adapt the deep learning techniques to hyperspectral face image super-resolution. We present a spectral splitting and aggregation network (SSANet) for HFSR with limited training samples.
  arXiv  Detail & Related papers  (2021-08-31T02:13:00Z)
• SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers [91.09957836250209]
  Hyperspectral (HS) images are characterized by approximately contiguous spectral information. CNNs have been proven to be a powerful feature extractor in HS image classification. We propose a novel backbone network called ulSpectralFormer for HS image classification.
  arXiv  Detail & Related papers  (2021-07-07T02:59:21Z)
• Spectral Analysis Network for Deep Representation Learning and Image Clustering [53.415803942270685]
  This paper proposes a new network structure for unsupervised deep representation learning based on spectral analysis. It can identify the local similarities among images in patch level and thus more robust against occlusion. It can learn more clustering-friendly representations and is capable to reveal the deep correlations among data samples.
  arXiv  Detail & Related papers  (2020-09-11T05:07:15Z)
• Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery [79.69449412334188]
  In this paper, we investigate how to adapt state-of-the-art residual learning based single gray/RGB image super-resolution approaches. We introduce a spatial-spectral prior network (SSPN) to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data. Experimental results on some hyperspectral images demonstrate that the proposed SSPSR method enhances the details of the recovered high-resolution hyperspectral images.
  arXiv  Detail & Related papers  (2020-05-18T14:25:50Z)

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.