Evaluation of ultrasonic fibrosis diagnostic system using convolutional network for ordinal regression

Saito, Ryosuke; Koizumi, Norihiro; Nishiyama, Yu; Imaizumi, Tsubasa; Kusahara, Kenta; Yagasaki, Shiho; Matsumoto, Naoki; Masuzaki, Ryota; Takahashi, Toshimi; Ogawa, Masahiro

doi:10.1007/s11548-021-02491-1

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…A study used two morphological image operations (dilation and erosion) to remove the noise and join disparate meaningful areas with continuous contours as the ROI of an image [ 24 ]; the selected ROI preserved all parenchyma areas and eliminated interference from parameters, body postures and other text information in the background, but blood vessels were involved. Saito et al used a U-Net to segment the liver parenchymal region and extracted ROIs with a size of 128 × 128 by sliding 12 pixels in the horizontal and vertical directions on the segmented area [ 25 ]. This method avoids blood vessels, but some blurred or useless areas may be included in the ROIs.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Siamese Network for Noninvasive Staging of Liver Fibrosis Based on US Image Pairs of the Liver and Spleen

Wang

Song

Zhuang

et al. 2023

Sensors

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Due to the heterogeneity of ultrasound (US) images and the indeterminate US texture of liver fibrosis (LF), automatic evaluation of LF based on US images is still challenging. Thus, this study aimed to propose a hierarchical Siamese network that combines the information from liver and spleen US images to improve the accuracy of LF grading. There were two stages in the proposed method. In stage one, a dual-channel Siamese network was trained to extract features from paired liver and spleen patches that were cropped from US images to avoid vascular interferences. Subsequently, the L1 distance was used to quantify the liver–spleen differences (LSDs). In stage two, the pretrained weights from stage one were transferred into the Siamese feature extractor of the LF staging model, and a classifier was trained using the fusion of the liver and LSD features for LF staging. This study was retrospectively conducted on US images of 286 patients with histologically proven liver fibrosis stages. Our method achieved a precision and sensitivity of 93.92% and 91.65%, respectively, for cirrhosis (S4) diagnosis, which is about 8% higher than that of the baseline model. The accuracy of the advanced fibrosis (≥S3) diagnosis and the multi-staging of fibrosis (≤S2 vs. S3 vs. S4) both improved about 5% to reach 90.40% and 83.93%, respectively. This study proposed a novel method that combined hepatic and splenic US images and improved the accuracy of LF staging, which indicates the great potential of liver–spleen texture comparison in noninvasive assessment of LF based on US images.

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Section: Introductionmentioning

confidence: 99%

A Hierarchical Siamese Network for Noninvasive Staging of Liver Fibrosis Based on US Image Pairs of the Liver and Spleen

Wang

Song

Zhuang

et al. 2023

Sensors

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“…In recent decades, various ordinal classification methods have been proposed by considering ordered labels within classification models, such as incremental learning model [14], support vector machine [15,16], deep learning model [17,18] and so on [19][20][21]. Now ordinal classification has been widely used in many fields such as pain assessment [22], consumer preference [23], medical research [24] and diagnostic system [25]. We have used neural network with ordered partitions (NNOP) [26] and support vector ordered regression [27] in apple grading problem 1 .…”

Section: Introductionmentioning

confidence: 99%

Apple grading method based on neural network with ordered partitions and evidential ensemble learning

Wei

et al. 2022

CAAI Trans on Intel Tech

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In order to improve the performance of the automatic apple grading and sorting system, in this paper, an ensemble model of ordinal classification based on neural network with ordered partitions and Dempster-Shafer theory is proposed. As a non-destructive grading method, apples are graded into three grades based on the Soluble Solids Content value, with features extracted from the preprocessed near-infrared spectrum of apple serving as model inputs. Considering the uncertainty in grading labels, mass generation approach and evidential encoding scheme for ordinal label are proposed, with uncertainty handled within the framework of Dempster-Shafer theory. Constructing neural network with ordered partitions as the base learner, the learning procedure of the Bagging-based ensemble model is detailed. Experiments on Yantai Red Fuji apples demonstrate the satisfactory grading performances of proposed evidential ensemble model for ordinal classification.

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“…Lei et al proposed a meta ordinal regression forest for early detection and classification of lung nodules and showing better performance over traditional binary classification (Lei et al 2020). Saito et al extract regions of liver parenchyma utilizing an ordinal regression model based on ResNet18 and get high accuracy (Saito et al 2021). Xu et al retrieve relative variables from electronic medical records to identify the independent predictors of illness severity.…”

Section: Introductionmentioning

confidence: 99%

An ensemble learning method based on ordinal regression for COVID-19 diagnosis from chest CT

Guo

Lei

et al. 2021

Phys. Med. Biol.

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Coronavirus disease 2019 (COVID-19) has brought huge losses to the world, and it remains a great threat to public health. X-ray computed tomography (CT) plays a central role in the management of COVID-19. Traditional diagnosis with pulmonary CT images is time-consuming and error-prone, which could not meet the need for precise and rapid COVID-19 screening. Nowadays, deep learning (DL) has been successfully applied to CT image analysis, which assists radiologists in workflow scheduling and treatment planning for patients with COVID-19. Traditional methods use cross-entropy as the loss function with a Softmax classifier following a fully-connected layer. Most DL-based classification methods target intraclass relationships in a certain class (early, progressive, severe, or dissipative phases), ignoring the natural order of different phases of the disease progression, i.e., from an early stage and progress to a late stage. To learn both intraclass and interclass relationships among different stages and improve the accuracy of classification, this paper proposes an ensemble learning method based on ordinal regression, which leverages the ordinal information on COVID-19 phases. The proposed method uses multi-binary, neuron stick-breaking (NSB), and soft labels (SL) techniques, and ensembles the ordinal outputs through a median selection. To evaluate our method, we collected 172 confirmed cases. In a 2-fold cross-validation experiment, the accuracy is increased by 22% compared with traditional methods when we use modified ResNet-18 as the backbone. And precision, recall, and F1-score are also improved. The experimental results show that our proposed method achieves a better classification performance than the traditional methods, which helps establish guidelines for the classification of COVID-19 chest CT images.

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Evaluation of ultrasonic fibrosis diagnostic system using convolutional network for ordinal regression

Cited by 6 publications

References 9 publications

A Hierarchical Siamese Network for Noninvasive Staging of Liver Fibrosis Based on US Image Pairs of the Liver and Spleen

A Hierarchical Siamese Network for Noninvasive Staging of Liver Fibrosis Based on US Image Pairs of the Liver and Spleen

Apple grading method based on neural network with ordered partitions and evidential ensemble learning

An ensemble learning method based on ordinal regression for COVID-19 diagnosis from chest CT

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