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 31 publications

(30 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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“…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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“…Verschiedene medizinische Anwendungen wurden bisher betrachtet: Die Erkennung von In-vivo-und Ex-vivo-Tumorgewebe am Ösophagus [23] und Darm (bisher unpubliziert), die Diskriminierung der Schilddrüse, Nebenschilddrüse und des N. laryngeus recurrens [24] sowie die Differenzierung des Gallengangs (D. hepatocholedochus) von der Gallenblase und der Leber. Klassische Verfahren des maschinellen Lernens, z.…”

Section: Hyperspektral Basierte "Enhanced Reality" (Hyper)unclassified

Neue intraoperative fluoreszenzbasierte und spektroskopische Bildgebungsverfahren in der Viszeralmedizin – Präzisionschirurgie im „Hightech“-OP

et al. 2021

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Zusammenfassung Hintergrund Die Fluoreszenz-Angiografie (FA) mit Indocyanin-Grün (ICG) und das Hyperspektral-Imaging (HSI) sind neue intraoperative Visualisierungsmöglichkeiten in der Abdominal-, Gefäß- und Transplantationschirurgie. Ziel ist es, damit die postoperative Morbidität und Mortalität im Sinne der Präzisionschirurgie und Patientensicherheit zu reduzieren. In diesem Artikel werden aktuelle Daten und das zukünftige innovative Potenzial der Echtzeit-Bildgebungsmodalitäten hervorgehoben. Methoden Das Prinzip der neuen „Imaging“-Verfahren und deren klinische Anwendungen werden aufgearbeitet mit Blick auf Anastomoseninsuffizienzen, die die folgenschwersten Komplikationen in der gastrointestinalen Chirurgie nach onkologischen Resektionen darstellen. Ergebnisse Während die FA die Gabe eines Fluoreszenzfarbstoffs erfordert, ist HSI völlig kontaktfrei und nichtinvasiv. Beide Methoden können in „Real-Time“-Applikation physiologische Gewebeeigenschaften, zudem die FA auch dynamische Phänomene, erfassen. Die Verfahren dauern nur wenige Sekunden und stören den operativen Ablauf nicht wesentlich. Sie sind hinsichtlich ihrer Aussagekraft für eine mögliche Änderung der operativen Strategie als komplementär zu betrachten. Unsere eigenen Weiterentwicklungen der Modalitäten liegen insbesondere im Bereich der Datenvisualisierung sowie der automatischen Datenanalyse unter Einsatz der künstlichen Intelligenz (KI) und der Miniaturisierung der bisherigen Geräte für einen zukünftigen Einsatz in der Endoskopie, der minimalinvasiven und der roboterassistierten Chirurgie. Schlussfolgerung Insgesamt ist die Zahl der Studien im Forschungsfeld der intraoperativen Bildgebung zurzeit noch sehr limitiert. Ob diese neue Art der Präzisionschirurgie im „Hightech“-OP mit der Kombination aus HSI, FA und Robotik zu einer verbesserten Patientensicherheit durch Minimierung der postoperativen Morbidität und Mortalität führt, muss in weiterführenden multizentrischen Studien evaluiert werden.

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Tissue classification of oncologic esophageal resectates based on hyperspectral data

Cited by 31 publications

References 22 publications

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Trends in Deep Learning for Medical Hyperspectral Image Analysis

Neue intraoperative fluoreszenzbasierte und spektroskopische Bildgebungsverfahren in der Viszeralmedizin – Präzisionschirurgie im „Hightech“-OP

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