A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images

Guo, Le‐Hang; Wang, Dan; Qian, Yiyi; Xiao, Zheng; Zhao, Chong‐Ke; Li, Xiaolong; Bo, Xiao-Wan; Yue, Wenwen; Zhang, Qi; Shi, Jun; Xu, Hui‐Xiong

doi:10.3233/ch-170275

Cited by 81 publications

(30 citation statements)

References 20 publications

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“…Since the different types of FLLs have different outcomes and require different clinical interventions, the current challenge in determining an accurate diagnosis involves not only effectively differentiating between benign and malignant FLLs according to the medical image but also accurately recognizing the different types of FLLs. A previous study[ 20 ] proposed a novel two-stage multiview learning framework for the ultrasound-based computer-aided diagnosis of benign and malignant liver tumors. Although both HCC and metastases are malignant liver tumors, their treatment strategies are completely different; thus, more accurate classification is needed.…”

Section: Discussionmentioning

confidence: 99%

Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography

Cao

Zhang

Kuang

et al. 2020

WJG

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BACKGROUND The accurate classification of focal liver lesions (FLLs) is essential to properly guide treatment options and predict prognosis. Dynamic contrast-enhanced computed tomography (DCE-CT) is still the cornerstone in the exact classification of FLLs due to its noninvasive nature, high scanning speed, and high-density resolution. Since their recent development, convolutional neural network-based deep learning techniques has been recognized to have high potential for image recognition tasks. AIM To develop and evaluate an automated multiphase convolutional dense network (MP-CDN) to classify FLLs on multiphase CT. METHODS A total of 517 FLLs scanned on a 320-detector CT scanner using a four-phase DCE-CT imaging protocol (including precontrast phase, arterial phase, portal venous phase, and delayed phase) from 2012 to 2017 were retrospectively enrolled. FLLs were classified into four categories: Category A, hepatocellular carcinoma (HCC); category B, liver metastases; category C, benign non-inflammatory FLLs including hemangiomas, focal nodular hyperplasias and adenomas; and category D, hepatic abscesses. Each category was split into a training set and test set in an approximate 8:2 ratio. An MP-CDN classifier with a sequential input of the four-phase CT images was developed to automatically classify FLLs. The classification performance of the model was evaluated on the test set; the accuracy and specificity were calculated from the confusion matrix, and the area under the receiver operating characteristic curve (AUC) was calculated from the SoftMax probability outputted from the last layer of the MP-CDN. RESULTS A total of 410 FLLs were used for training and 107 FLLs were used for testing. The mean classification accuracy of the test set was 81.3% (87/107). The accuracy/specificity of distinguishing each category from the others were 0.916/0.964, 0.925/0.905, 0.860/0.918, and 0.925/0.963 for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively. The AUC (95% confidence interval) for differentiating each category from the others was 0.92 (0.837-0.992), 0.99 (0.967-1.00), 0.88 (0.795-0.955) and 0.96 (0.914-0.996) for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively. CONCLUSION MP-CDN accurately classified FLLs detected on four-phase CT as HCC, metastases, benign non-inflammatory FLLs and hepatic abscesses and may assist radiologists in identifying the different types of FLLs.

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Section: Discussionmentioning

confidence: 99%

Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography

Cao

Zhang

Kuang

et al. 2020

WJG

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“…Several studies have applied deep learning to liver US imaging to detect or characterize focal liver lesions [13][14][15][16][17]. These studies are summarized in Table 2.…”

Section: Focal Liver Diseasementioning

confidence: 99%

“…In terms of the type of data used, contrast-enhanced US (CEUS) was used to develop deep learning models in three studies [13,15,17]. Among them, Liu et al [17] and Pan et al (Fig.…”

Section: Focal Liver Diseasementioning

confidence: 99%

Current status of deep learning applications in abdominal ultrasonography

Song

2021

Ultrasonography

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Ultrasonography. 2020 Sep 2. Epub ahead of print Deep learning is one of the most popular artificial intelligence techniques used in the medical field. Although it is at an early stage compared to deep learning analyses of computed tomography or magnetic resonance imaging, studies applying deep learning to ultrasound imaging have been actively conducted. This review analyzes recent studies that applied deep learning to ultrasound imaging of various abdominal organs and explains the challenges encountered in these applications.

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“…So wurde gezeigt, dass sonografische Daten unklarer Leberläsionen mit hoher Genauigkeit einzelnen Entitäten zugeordnet werden konnten [10]. In der Kombination von kontrastmittelbasiertem Ultraschall (CEUS) und KI ließ sich die Genauigkeit in einer weiteren Studie noch erhöhen [11].…”

Section: Klinische Anwendungenunclassified

Künstliche Intelligenz in der Gastroenterologie

Kather

Krause

Luedde

2020

Dtsch Med Wochenschr

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Was ist neu? Maschinelles Lernen und künstliche Intelligenz Künstliche Intelligenz (KI) verändert derzeit alle Bereiche unseres Lebens- und Arbeitsalltags. Die Welt der Medizin ist hiervon nicht ausgenommen. Eine Kernmethode der künstlichen Intelligenz – künstliche neuronale Netzwerke – ist besonders effektiv im Analysieren von Bilddaten. Dies beeinflusst auch die Gastroenterologie wesentlich. Klinische Anwendungen Insbesondere endoskopische, radiologische und histologische Bilder können mittels künstlicher neuronaler Netzwerke automatisch analysiert werden. Einerseits können dadurch repetitive Tätigkeiten automatisiert werden, beispielsweise die Suche nach Polypen in der Screening-Koloskopie. Andererseits ermöglicht dies jedoch auch die Erkennung von subtilen Mustern in Bilddaten, welche als neuartige Biomarker für klinische Verläufe genutzt werden könnten. Ausblick Das Angebot kommerzieller KI-Systeme wird in Zukunft deutlich zunehmen. Wichtig ist die Ausbildung interdisziplinärer Forscherinnen und Forscher, das Vorhandensein standardisierter Datenbanken und das frühzeitige Heranführen der Ärztinnen und Ärzte an die komplexen KI-Anwendungen.

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A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images

Cited by 81 publications

References 20 publications

Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography

Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography

Current status of deep learning applications in abdominal ultrasonography

Künstliche Intelligenz in der Gastroenterologie

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