Tissue classification of oncologic esophageal resectates based on hyperspectral data

Maktabi, Marianne; Köhler, Helmut; Ivanova, Margarita; Jansen-Winkeln, Boris; Takoh, JP; Niebisch, Stefan; Rabe, SM; Neumuth, Thomas; Gockel, Ines; Chalopin, Claire

doi:10.1007/s11548-019-02016-x

Cited by 36 publications

(36 citation statements)

References 22 publications

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“…C4.5. The three commonly used decision tree algorithms [36] are Iterative Dichotomiser (ID3), C4.5, and CART. Among them, decision tree C4.5 was an improvement on ID3.…”

Section: Knn Knnmentioning

confidence: 99%

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Xiong

2021

Computational and Mathematical Methods in Medicine

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Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based feature selection (FCBF) method was used to preprocess the data to eliminate irrelevant and redundant features. Then, the classification was carried out in the stacking ensemble learner. A library for support vector machine (LIBSVM), K -nearest neighbor (KNN), decision tree C4.5 (C4.5), and random forest (RF) were used as the primary learners of the stacking ensemble. Given the imbalanced characteristics of cancer gene expression data, the embedding cost-sensitive naive Bayes was used as the metalearner of the stacking ensemble, which was represented as CSNB stacking. The proposed CSNB stacking method was applied to nine cancer datasets to further verify the classification performance of the model. Compared with other classification methods, such as single classifier algorithms and ensemble algorithms, the experimental results showed the effectiveness and robustness of the proposed method in processing different types of cancer data. This method may therefore help guide cancer diagnosis and research.

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“…C4.5. The three commonly used decision tree algorithms [36] are Iterative Dichotomiser (ID3), C4.5, and CART. Among them, decision tree C4.5 was an improvement on ID3.…”

Section: Knn Knnmentioning

confidence: 99%

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Xiong

2021

Computational and Mathematical Methods in Medicine

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“…The answer is YES. Maktabi et al[ 34 ] tested a relatively new hyperspectral imaging system. They found that SVM was able to detect cancerous tissue with 63% SEN and 69% SPE within 1s.…”

Section: Implications For Diagnosis and Therapeutic Decisionsmentioning

confidence: 99%

Artificial intelligence-assisted esophageal cancer management: Now and future

Zhang

Guo

Yuan

et al. 2020

WJG

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Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert’s performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators.

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“…Cell classification using convolutional neural networks in medical hyperspectral imagery [45] Classification Hyperspectral imaging for cancer detection and classification [47] Medical hyperspectral imaging: a review [7] Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging [35] Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model [55] Convolutional neural network for medical hyperspectral image classification with kernel fusion [51] Classification Blood cell classification based on hyperspectral imaging with modulated Gabor and CNN [52] Medical hyperspectral image classification based on end-to-end fusion deep neural network [53] Tissue classification of oncologic esophageal resectates based on hyperspectral data [49] Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm [57] Design of a multilayer neural network for the classification of skin ulcers' hyperspectral images: a proof of concept [48] Hyperspectral imaging based method for fast characterization of kidney stone types [46] Design of a multilayer neural network for the classification of skin ulcers' hyperspectral images: a proof of concept [83] Non-invasive skin cancer diagnosis using hyperspectral imaging for in-situ clinical support [50] Hyperspectral imaging for colon cancer classification in surgical specimens: towards optical biopsy during image-guided surgery [84] Deep learning applied to hyperspectral endoscopy for online spectral classification [85] Spectral-spatial recurrent-convolutional networks for in-vivo hyperspectral tumor type classification [56] Blood stain classification with hyperspectral imaging and deep neural networks [54] Hyperspectral imaging for glioblastoma surgery: improving tumor identification using a deep spectral-spatial approach [58] Dual-modality endoscopic probe for tissue surface shape reconstruction and hyperspectral imaging enabled by deep neural networks [62] Detection Probe-based rapid hybrid hyperspectral and tissue surface imaging aided by fully convolutional networks [63] A dual stream network for tumor detection in hyperspectral images [59] Adaptive deep learning for head and neck cancer detection using hyperspectral imaging [67] Detection Hyperspectral imaging of head and neck squamous cell carcinoma for can...…”

Section: Publication Title Categorymentioning

confidence: 99%

Trends in Deep Learning for Medical Hyperspectral Image Analysis

et al. 2021

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Deep learning algorithms have seen acute growth of interest in their applications throughout several fields of interest in the last decade, with medical hyperspectral imaging being a particularly promising domain. So far, to the best of our knowledge, there is no review paper that discusses the implementation of deep learning for medical hyperspectral imaging, which is what this work aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery. This paper discusses deep learning concepts that are relevant and applicable to medical hyperspectral imaging analysis, several of which have been implemented since the boom in deep learning. This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging. Lastly, we discuss the current and future challenges pertaining to this discipline and the possible efforts to overcome such trials.INDEX TERMS Deep learning, neural networks, machine learning, medical image analysis, medical hyperspectral imaging.

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Tissue classification of oncologic esophageal resectates based on hyperspectral data

Cited by 36 publications

References 22 publications

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Artificial intelligence-assisted esophageal cancer management: Now and future

Trends in Deep Learning for Medical Hyperspectral Image Analysis

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