Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to MRI Liver Tumor Differentiation

Trivizakis, Eleftherios; Manikis, Georgios C.; Nikiforaki, Katerina; Drevelegas, Konstantinos; Constantinides, Manos; Drevelegas, Antonios; Marias, Kostas

doi:10.1109/jbhi.2018.2886276

Cited by 120 publications

(81 citation statements)

References 21 publications

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“…A source model trained with widely available 'natural' images can be transferred to a target model that will perform similar tasks but in the medical imaging domain. The learnt feature detectors of these deep architectures as a result of their low-level status can be an alternative and viable (22) MGMT state 94.9/-Grinband et al (19) MGMT state 83/84 Akkus et al (23) 1p19q codeletion status 87.7/-Grinband et al (19) 1p19q codeletion status 92/88 Bonte et al (25) Glioma grading 91.1,93.5/82,86.1 Zhou et al (27) Metastatic/glioma/meningioma 92.1/-Momeni et al (28) Oligendroglioma/astrocytoma 85/92 Afshar et al (29) Glioma/pituitary/meningioma 86.6/-Yu et al (31) EGFR mutation status 76.1/82.8 Wang et al (32) EGFR mutation status 73.9/81 Zhu et al 34Luminal A vs others -/58-65 Ha et al (35) Luminal A vs. B vs. HER2 + vs. Basal 70/87.1 Yoon et al (36) Pathological state, ER, PR, HER2 -/69.7, 97.6, 89.9, 84.2 Zhu et al (37) Occult invasive disease status -/70 Ypsilantis et al (8) Neoadjuvant chemotherapy response 73.4/66.3 Bibault et al (38) Neoadjuvant chemoradiation response 80/72 Chen et al (39) Subtype prediction 80, voting: 92.3/-Trivizakis et al (12) Primary/metastasis 83/80 Cha et al (40) Chemotherapy response -/62-77 Cha et al (41) Chemotherapy response -/62-79 Banerjee et al (42) Subtype prediction 85/-Zhou et al (43) Lymph node metastasis 72.7-93/65-92 IDH1, isocitrate dehydrogenase isozyme 1; MGMT, methylguanine methyltransferase; EGFR, epidermal growth factor receptor; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; ACC, accuracy; AUC, area under the curve.…”

Section: Multi-model Decision Fusionmentioning

confidence: 99%

“…The diagnosis of liver cancer with traditional machine learning techniques is extremely challenging considering its multifocal distribution. Tissue discrimination between primary and metastatic liver cancer was performed by utilizing 3D-CNN (12) with no pre-processing or segmentation from raw high b-value MRI volumes (b=1,000 sec/mm 2 ) achieving state-of-the-art performance through a fully automated analysis.…”

Section: Non-small Cell Lung Cancer (Nsclc)mentioning

confidence: 99%

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Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

et al. 2020

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The new era of artificial intelligence (AI) has introduced revolutionary data-driven analysis paradigms that have led to significant advancements in information processing techniques in the context of clinical decision-support systems. These advances have created unprecedented momentum in computational medical imaging applications and have given rise to new precision medicine research areas. Radiogenomics is a novel research field focusing on establishing associations between radiological features and genomic or molecular expression in order to shed light on the underlying disease mechanisms and enhance diagnostic procedures towards personalized medicine. The aim of the current review was to elucidate recent advances in radiogenomics research, focusing on deep learning with emphasis on radiology and oncology applications. The main deep learning radiogenomics architectures, together with the clinical questions addressed, and the achieved genetic or molecular correlations are presented, while a performance comparison of the proposed methodologies is conducted. Finally, current limitations, potentially understudied topics and future research directions are discussed. Contents 1. Introduction 2. Research methodology 3. Deep architectures used in current radiogenomics studies 4. Clinical applications of deep learning-based radiogenomics 5. Limitations of radiogenomic research 6. Discussion and future directions

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Section: Multi-model Decision Fusionmentioning

confidence: 99%

Section: Non-small Cell Lung Cancer (Nsclc)mentioning

confidence: 99%

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

et al. 2020

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“…[51][52][53] For example, Chang et al proposed a framework of multiple residual convolutional neural networks to noninvasively predict isocitrate dehydrogenase genotype in grades II-IV glioma using multi-institutional magnetic resonance imaging datasets. Besides, AI has been used in discovering radiogenomic associations in breast cancer, 52 liver cancer, 54 and colorectal cancer. 53 Currently, limited data availability remains the most formidable challenge for AI radiogenomics.…”

Section: Future Synergies Between Ai and Precision Medicinementioning

confidence: 99%

Precision Medicine, AI, and the Future of Personalized Health Care

Johnson

Wei

Weeraratne

et al. 2020

Clinical Translational Sci

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227

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The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less‐common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.

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“…As shown in equation (9) where and are their biases and is the weight, are the binary states of visible unit j and the hidden unit i. Through this energy function the network allocates a likelihood to any pair of a visible and hidden layer:…”

Section: Mathematical Analysis Using Preposition 3: Fully Convolutmentioning

confidence: 99%

“…Furthermore, the scope of this challenge illustrates the need for computerized analytics to help clinicians diagnose, detect, and evaluate hepatic metastases in CT tests [9]. Due to the various contrast actions of hepatic and parenchymatic lesions, automatic identification and segmentation have been extremely challenging [10].…”

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confidence: 99%

Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework

et al. 2020

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Liver cancer is one of the world's largest causes of death to humans. It is a difficult task and time consuming to identify the cancer tissue manually in the present scenario. The segmentation of liver lesions in CT images can be used to assess the tumor load, plan treatments predict, and monitor the clinical response. In this paper, the Hybridized Fully Convolutional Neural Network (HFCNN) has been proposed for liver tumor segmentation, which has been modeled mathematically to resolve the current issue of liver cancer. For semantic segmentation, HFCNN has been used as a powerful tool for liver cancer analysis. Whereas the CT-based lesion-type definition defines the diagnosis and therapeutic strategy, the distinction between cancer and non-cancer lesions is crucial. It demands highly qualified experience, expertise, and resources. However, a deep end-to-end learning approach to help discrimination in abdominal CT images of the liver between liver metastases of colorectal cancer and benign cysts has been analyzed. Our method includes the successful extraction of features from Inception combined with residual and pre-trained weights. Feature maps have been consistent with the original image voxel features, and The importance of features seemed to represent the most relevant imaging criteria for every class. This deep learning system shows the concept of illumination portions of the decision-making process of a pre-trained deep neural network, through an analysis of inner layers and the description of features that lead to predictions.

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Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to MRI Liver Tumor Differentiation

Cited by 120 publications

References 21 publications

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

Precision Medicine, AI, and the Future of Personalized Health Care

Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework

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