Using hyperspectral imaging automatic classification of gastric cancer grading with a shallow residual network

Liu, Song; Wang, Quan; Zhang, Geng; Du, Jian; Hu, Bingliang; Zhang, Zhoufeng

doi:10.1039/d0ay01023e

Cited by 8 publications

(8 citation statements)

References 26 publications

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“…In FD processing, as used by Amigo et al., 18 Hu et al., 40 Liu et al., 39 Peñaranda et al., 16 and Mellors et al., 41 the FD of the spectrum, instead of the spectrum itself, is used for tissue classification: RFD(λ)=dRtot(λ)dλ.FD processing results in a characterization of the localized slope of the spectrum. FD processing is very sensitive to noise.…”

Section: Methodsmentioning

confidence: 99%

“…FD processing is very sensitive to noise. Therefore, in general, filtering or smoothing of the spectrum is performed before FD processing, such as Savitzky–Golay filtering 16 , 18 , 39 – 41 In our analysis, we used Savitzky–Golay filtering and optimized the window size so it would result in the lowest overlap coefficient, which was a window size of 199 nm.…”

Section: Methodsmentioning

confidence: 99%

“…In FD processing, as used by Amigo et al, 18 Hu et al, 40 Liu et al, 39 Peñaranda et al, 16 and Mellors et al, 41 the FD of the spectrum, instead of the spectrum itself, is used for tissue classification:…”

Section: First Derivativementioning

confidence: 99%

“…By a literature search, we identified eight commonly used preprocessing algorithms applied to medical hyperspectral images: standard normal variate (SNV), 10 – 17 multiplicative scatter correction (MSC), 18 – 20 min-max normalization (MM), 16 , 21 – 29 mean centering (MC), 30 , 31 area under the curve normalization (AUC), 32 – 37 single wavelength normalization (SW), 1 , 38 first derivative (FD), 16 , 18 , 39 – 41 and second derivative (SD) 42 , 43 . The details of each algorithm are discussed in Sec.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

et al. 2022

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Significance: Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the development of these algorithms, data are generally preprocessed to remove variability in data not related to the tissue itself since this will improve the performance of the classification algorithm. In hyperspectral imaging, the measured spectra are also influenced by reflections from the surface (glare) and height variations within and between tissue samples.Aim: To compare the ability of different preprocessing algorithms to decrease variations in spectra induced by glare and height differences while maintaining contrast based on differences in optical properties between tissue types.Approach: We compare eight preprocessing algorithms commonly used in medical hyperspectral imaging: standard normal variate, multiplicative scatter correction, min-max normalization, mean centering, area under the curve normalization, single wavelength normalization, first derivative, and second derivative. We investigate conservation of contrast stemming from differences in: blood volume fraction, presence of different absorbers, scatter amplitude, and scatter slope-while correcting for glare and height variations. We use a similarity metric, the overlap coefficient, to quantify contrast between spectra. We also investigate the algorithms for clinical datasets from the colon and breast.Conclusions: Preprocessing reduces the overlap due to glare and distance variations. In general, the algorithms standard normal variate, min-max, area under the curve, and single wavelength normalization are the most suitable to preprocess data used to develop a classification algorithm for tissue classification. The type of contrast between tissue types determines which of these four algorithms is most suitable.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: First Derivativementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

et al. 2022

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“…HSIs contain rich spectral information. Meanwhile, the 2D convolution neural networks have significantly affected computer vision, with applications including biomedical image classification [37], remote sensing image classification [3,38], change detection [39,40], and image deblurring [2]. However, when convolving along the spectral dimension of HSI, hundreds of bands need plenty of parameters.…”

Section: Introductionmentioning

confidence: 99%

Spectral-Spatial Attention Rotation-Invariant Classification Network for Airborne Hyperspectral Images

Shi

Wang

et al. 2023

Drones

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An airborne hyperspectral imaging system is typically equipped on an aircraft or unmanned aerial vehicle (UAV) to capture ground scenes from an overlooking perspective. Due to the rotation of the aircraft or UAV, the same region of land cover may be imaged from different viewing angles. While humans can accurately recognize the same objects from different viewing angles, classification methods based on spectral-spatial features for airborne hyperspectral images exhibit significant errors. The existing methods primarily involve incorporating image or feature rotation angles into the network to improve its accuracy in classifying rotated images. However, these methods introduce additional parameters that need to be manually determined, which may not be optimal for all applications. This paper presents a spectral-spatial attention rotation-invariant classification network for the airborne hyperspectral image to address this issue. The proposed method does not require the introduction of additional rotation angle parameters. There are three modules in the proposed framework: the band selection module, the local spatial feature enhancement module, and the lightweight feature enhancement module. The band selection module suppresses redundant spectral channels, while the local spatial feature enhancement module generates a multi-angle parallel feature encoding network to improve the discrimination of the center pixel. The multi-angle parallel feature encoding network also learns the position relationship between each pixel, thus maintaining rotation invariance. The lightweight feature enhancement module is the last layer of the framework, which enhances important features and suppresses insignificance features. At the same time, a dynamically weighted cross-entropy loss is utilized as the loss function. This loss function adjusts the model’s sensitivity for samples with different categories according to the output in the training epoch. The proposed method is evaluated on five airborne hyperspectral image datasets covering urban and agricultural regions. Compared with other state-of-the-art classification algorithms, the method achieves the best classification accuracy and is capable of effectively extracting rotation-invariant features for urban and rural areas.

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Identification of gastric cancer types based on hyperspectral imaging technology

Tian,

Su,

Huang

et al. 2023

Journal of Biophotonics

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AbtractGastric cancer is becoming the second biggest cause of death from cancer. Treatment and prognosis of different types of gastric cancer vary greatly. However, the routine pathological examination is limited to the tissue level and is easily affected by subjective factors. In our study, we examined gastric mucosal samples from 50 normal tissue and 90 cancer tissues. Hyperspectral imaging technology was used to obtain spectralinformation. A two‐classification model for normal tissue and cancer tissue identification and a four‐classification model for cancer type identification are constructed based on the improved deep residual network(IDRN). The accuracy of the two‐classification model and four‐classification model are 0.947 and 0.965. Hyperspectral imaging technology was used to extract molecular information to realize real‐time diagnosis and accurate typing. The results show that hyperspectral imaging technique has good effect on diagnosis and type differentiation of gastric cancer, which is expected to be used in auxiliary diagnosis and treatment.This article is protected by copyright. All rights reserved.

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Using hyperspectral imaging automatic classification of gastric cancer grading with a shallow residual network

Abstract:
The gastric cancer grading of a patient determines their clinical treatment plan. We use hyperspectral imaging (HSI) gastric cancer section data to automatically classify the three different cancer grades (low...

Cited by 8 publications

References 26 publications

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

Spectral-Spatial Attention Rotation-Invariant Classification Network for Airborne Hyperspectral Images

Identification of gastric cancer types based on hyperspectral imaging technology

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Using hyperspectral imaging automatic classification of gastric cancer grading with a shallow residual network

Abstract: The gastric cancer grading of a patient determines their clinical treatment plan. We use hyperspectral imaging (HSI) gastric cancer section data to automatically classify the three different cancer grades (low...

Cited by 8 publications

References 26 publications

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

Spectral-Spatial Attention Rotation-Invariant Classification Network for Airborne Hyperspectral Images

Identification of gastric cancer types based on hyperspectral imaging technology

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Product

Resources

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Abstract:
The gastric cancer grading of a patient determines their clinical treatment plan. We use hyperspectral imaging (HSI) gastric cancer section data to automatically classify the three different cancer grades (low...