Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma

Miyoshi, Hiroaki; Sato, Kensaku; Kabeya, Yoshinori; Yonezawa, S.; Nakano, H.; Takeuchi, Yusuke; Ozawa, I.; Higo, Shoichi; Yanagida, Eriko; Yamada, Kyohei; Kohno, Kei; Furuta, Takeshi; Muta, Hiroko; Takeuchi, Mai; Sasaki, Yo; Yoshimura, Takuro; Matsuda, Kazunobu; Muto, Reiji; Moritsubo, Mayuko; Inoue, Kanako; Suzuki, Takaharu; Sekinaga, Hiroaki; Ohshima, Koichi

doi:10.1038/s41374-020-0442-3

Cited by 57 publications

(58 citation statements)

References 14 publications

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“…The CAD system can be considered a 'second opinion' to help radiologists and dermatologists in deciding [6,7]. Decreasing the workload, reducing the false-negative diagnosis due to probabilistic physician mistakes, and avoiding the overloaded ignoring are the main advantages of CAD systems [8,9]. These methods usually involve three significant steps: i.…”

Section: Background and Motivationsmentioning

confidence: 99%

“…When the TS sample number is low and the noise affects TS data, ApEn is a suitable tool for quantifying the irregularity and complexity rate [40]. ApEn can be defined as (8).…”

Section: I) Approximate Entropy (Apen)mentioning

confidence: 99%

“…Cross-validation is presented to solve the problem of dependency of classification accuracy to the training dataset. By this approach, the effect of over-fitting could be eliminated, and the classification is performed more reliable [8]. Partitioning all data into the K-fold, employing K-1 folds to train the classifier, and applying the rest to validate are the fundamental k-fold cross-validation steps.…”

Section: K-fold Stratified Cross Validationmentioning

confidence: 99%

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A Machine Learning Method Based on the Combination of Nonlinear and Texture Features to Diagnose Malignant Melanoma From Dermoscopic Images

Ghahfarrokhi

Khodadadi

2021

Preprint

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Skin cancer affects people of all skin tones, including those with darker complexions. Melanomas are known as the malignant tumors of skin cancer, resulting in an adverse prognosis, responsible for most deaths relating to skin cancer. Early diagnosis and treatment of skin cancer from dermoscopic images can significantly reduce mortality and save lives. Although several Computer-Aided Diagnosis (CAD) systems with satisfactory performance have been introduced in the literature for skin cancer detection, the high false detection rate has made it inevitable to have an expert physician for more examination. In this paper, a CAD system based on machine learning algorithms is provided to classify various skin cancer types. The proposed method uses the Online Region-based Active Contour Model (ORACM) to extract the Region Of Interest (ROI) of skin lesions. This model uses a new binary level set equation and regularization operation such as morphological opening and closing.Additionally, various combinations of different textures and nonlinear features are extracted for the ROI to show the multiple aspects of skin lesions. Several metaheuristic optimization algorithms are used to remove redundant or irrelevant features and reduce the feature space dimension. These are applied to the combination of the extracted features in which, Non-dominated Sorting Genetic Algorithm (NSGA II) as a multi-objective optimization algorithm has the best performance. Furthermore, various machine learning algorithms include K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Fitting neural network (Fit net), Feed-Forward neural network (FF net), and Pattern recognition network (Pat net) are employed for the classification. Accordingly, the best-obtained precision of 99.24% based on five-fold cross-validation is attained by the selected features of texture and nonlinear indices through NSGA II, applying the pattern net classifier. Also, the comparison between this paper's experimental results and other similar works with the same dataset demonstrates the proposed method's efficiency.

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Section: Background and Motivationsmentioning

confidence: 99%

“…When the TS sample number is low and the noise affects TS data, ApEn is a suitable tool for quantifying the irregularity and complexity rate [40]. ApEn can be defined as (8).…”

Section: I) Approximate Entropy (Apen)mentioning

confidence: 99%

Section: K-fold Stratified Cross Validationmentioning

confidence: 99%

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A Machine Learning Method Based on the Combination of Nonlinear and Texture Features to Diagnose Malignant Melanoma From Dermoscopic Images

Ghahfarrokhi

Khodadadi

2021

Preprint

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“…Diagnostic accuracy equivalent to that of dermatologists was achieved by using a convolutional neural network for the classification of skin cancer 5 . The application of DL to histopathological tissue samples has been advanced, and DL was used for the discrimination of malignant lymphoma 6 and breast cancer 7 . Although the detection of malignant thyroid and salivary gland tumors using DL has already been reported for histopathological samples 8 , there have been no reports on DL for the discrimination of parotid gland tumors using MRI images, except for reports on texture analysis 9 , 10 .…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Diagnostic accuracy of deep-learning with anomaly detection for a small amount of imbalanced data: discriminating malignant parotid tumors in MRI

Matsuo

Nishio

Kanda

et al. 2020

Sci Rep

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We hypothesized that, in discrimination between benign and malignant parotid gland tumors, high diagnostic accuracy could be obtained with a small amount of imbalanced data when anomaly detection (AD) was combined with deep leaning (DL) model and the L2-constrained softmax loss. The purpose of this study was to evaluate whether the proposed method was more accurate than other commonly used DL or AD methods. Magnetic resonance (MR) images of 245 parotid tumors (22.5% malignant) were retrospectively collected. We evaluated the diagnostic accuracy of the proposed method (VGG16-based DL and AD) and that of classification models using conventional DL and AD methods. A radiologist also evaluated the MR images. ROC and precision-recall (PR) analyses were performed, and the area under the curve (AUC) was calculated. In terms of diagnostic performance, the VGG16-based model with the L2-constrained softmax loss and AD (local outlier factor) outperformed conventional DL and AD methods and a radiologist (ROC-AUC = 0.86 and PR-ROC = 0.77). The proposed method could discriminate between benign and malignant parotid tumors in MR images even when only a small amount of data with imbalanced distribution is available.

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“…It has previously been shown that subtyping of carcinoma is feasible [ 10 , 11 , 12 , 13 ]. However, few reports are available on the classification of hematological neoplasms, particularly NHL subtypes [ 14 , 15 , 16 , 17 , 18 ]. Therefore, we set out to investigate whether the classification of tumor-free LNs, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia (SLL/CLL), and nodal diffuse large B-cell lymphoma (DLBCL) is possible using deep learning techniques on scanned histopathological slides.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images

Steinbuß

Kriegsmann

Zgorzelski

et al. 2021

Cancers

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The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma. The optimized algorithm achieved an accuracy of 95.56% on an independent test set including 16,960 image patches from 125 patients after the application of quality controls. Automatic classification of NHL is possible with high accuracy using deep learning on histopathological images and routine diagnostic applications should be pursued.

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Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma

Cited by 57 publications

References 14 publications

A Machine Learning Method Based on the Combination of Nonlinear and Texture Features to Diagnose Malignant Melanoma From Dermoscopic Images

A Machine Learning Method Based on the Combination of Nonlinear and Texture Features to Diagnose Malignant Melanoma From Dermoscopic Images

Diagnostic accuracy of deep-learning with anomaly detection for a small amount of imbalanced data: discriminating malignant parotid tumors in MRI

Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images

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