Cross-View Self-Similarity Using Shared Dictionary Learning for Cervical Cancer Staging

Bnouni, Nesrine; Rekik, Islem; Rhim, Mohamed Salah; Amara, Najoua Essoukri Ben

doi:10.1109/access.2019.2902654

Cited by 10 publications

(4 citation statements)

References 40 publications

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“…The dataset used in this work is moderately small. For staging predictions in cervical cancer, the study [7] designed CVSS dictionary learning framework by means of multi-view MR images. This work demonstrated the results of classification accuracy in identifying the stages of cervical cancer, however the accuracy is not reasonable.…”

Section: Literature Studymentioning

confidence: 99%

Optimized Machine Learning based Classifications of Staging in Gynecological Cancers using Feature Subset through Fused Feature Selection Process

Nithya¹,

Ilango²

2020

IJACSA

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After diagnosing the cancer, the next step is to identify the staging of the cancer to start with the appropriate treatment plans. There are different kinds of gynaecological cancers and this research lays emphasis on cervical and ovarian cancer types with their staging classifications. The cervical and ovarian cancers data from SEER registry are used in this work. This work intends to propose an optimized classification method for staging prediction in gynaecological cancers through fused feature selection process that aimed to provide an optimal feature subset. The fused feature selection process includes the hybridization of relief filter approach with wrapper method of genetic algorithm to produce revised feature subset of data as an outcome. Accordingly, this work attained an improved feature subset through fused feature selection process for precise classification of cervical and ovarian cancer stages by identifying their significant features. The predictive models are established with 10-fold cross validation using major classification algorithms like C5.0, Random Forest and KNN. The classification results are attained for the respective types of cervical, ovarian cancer stages and the stage-wise classification based on patients age also obtained through this proposed method. The results portrayed that the women in the age group of 45 and above are more critical with the incidence of cervical and ovarian cancer types. Random Forest method has shown progressive accuracy rate with progressive percentage of other performance outcomes. Also, this work recognized that the best and optimal feature subset selection could condense the complexity of the predictive model.

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Section: Literature Studymentioning

confidence: 99%

Optimized Machine Learning based Classifications of Staging in Gynecological Cancers using Feature Subset through Fused Feature Selection Process

Nithya¹,

Ilango²

2020

IJACSA

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“…Endometrial and cervical cancer are the mostly common gynecologic malignancies in the world. The clinical stage [1] is based on the prognostic factors like the tumor volume [2] and the nodal status. Cancer can spread to other part of the body and/or through the lymph system.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging

Bnouni

Rekik

Rhim

et al. 2020

IJCDS

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Pelvic Lymph Nodes (PLNs) segmentation and classification are fundamental tools in the medical image analysis of pelvic gynecological cancer such as endometrial and cervical cancer. Often used by the radiologist, PLN classification requires detailed knowledge of the morphological features of PLNs, derived from size, shape, contour and heterogeneous appearance. Accurate PLN segmentation is an essential step in PLN classification. In order to supply the best assessment of a nodal status, semiautomatic and automatic PLN segmentation and classification methods are highly desired as they can strongly capture the wide variability in morphological features and reduce classification errors due to the inter and intra-observer variability, while avoiding the time-consuming for manual delineation of PLN boundary. Nevertheless, semi-automatic segmentation methods require the clinician intervention to select the initial seed point. However, typical semi-automatic PLN segmentation methods might fail due to (1) the intensity inhomogeneity, noise and low contrast in medical images, and (2) the position of the starting point. Thus, the performance of these methods can be enhanced by using a preprocessing-based iterative segmentation approach. Currently, Magnetic Resonance Imaging (MRI) is the most common imaging modality used for staging endometrial and cervical cancer, evaluation of PLN involvement and selection of therapeutic strategy. PLN detection using classic features can be challenging due to the similarity between normal and abnormal PLNs structures. In pelvic cancer and metastatic PLN, Diffusion Weighted (DW)-MRI exhibits brighter areas indicating tumor and metastatic PLN. This paper combines anatomic T2-Weighted (T2-w) imaging with DW imaging. Specifically, we propose a computer-aided pelvic framework, which leverages (1) an ensemble preprocessing method to improve PLN segmentation, (2) the iterative correction of the position of the initial point by executing the segmentation algorithm several times in succession, (3) the fusion of structural and diffusion MRI and, (4) the extraction of morphological features of segmented PLNs (axial T2-w image) as well as intensity feature derived from the fused image for the final classification of PLNs as suspect or non-suspect. Research in the field of PLN detection is important as it can help doctors to better detect cervical and endometrial cancer and decide the appropriate treatment. To the best of our knowledge, this is the first work to segment and classify PLN. Our preprocessing-based iterative segmentation approach significantly (p<0.05) improved comparison segmentation methods, with a segmentation accuracy boosted from 61.37% for the conventional region-growing algorithm to 66.53% for the proposed method. Furthermore, we obtained an average accuracy of 78.50% for pelvic nodule classification.

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“…Magnetic Resonance Imaging (MRI) of the pelvis is the most reliable imaging modality for staging, treatment planning, and following-up the cervical cancer. In particular, clinical staging of cervical cancer is based on the nodal status [2] and the tumor volume. However, this heavily relies on developing accurate segmentation techniques of cervical cancer on MR images that can effectively capture the large variability in the shape, location, and size of the tumor.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Multi-scale CNN Forest Learning for Automatic Cervical Cancer Segmentation

Bnouni

Rekik

Rhim

et al. 2018

Machine Learning in Medical Imaging

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Deep-learning based labeling methods have gained unprecedented popularity in different computer vision and medical image segmentation tasks. However, to the best of our knowledge, these have not been used for cervical tumor segmentation. More importantly, while the majority of innovative deeplearning works using convolutional neural networks (CNNs) focus on developing more sophisticated and robust architectures (e.g., ResNet, U-Net, GANs), there is very limited work on how to aggregate different CNN architectures to improve their relational learning at multiple levels of CNN-to-CNN interactions. To address this gap, we introduce a Dynamic Multi-Scale CNN Forest (C K+1 DMF), which aims to address three major issues in medical image labeling and ensemble CNN learning: (1) heterogeneous distribution of MRI training patches, (2) a bidirectional flow of information between two consecutive CNNs as opposed to cascading CNNs-where information passes in a directional way from current to the next CNN in the cascade, and (3) multiscale anatomical variability across patients. To solve the first issue, we group training samples into clusters, then design a forest with (+ 1) trees: a principal tree of CNNs trained using all data samples and subordinate trees, each trained using a cluster of samples. As for the second and third issues, we design each dynamic multiscale tree (DMT) in the forest such that each node in the tree nests a CNN architecture. Two successive CNN nodes in the tree pass bidirectional contextual maps to progressively improve the learning of their relational non-linear mapping. Besides, as we traverse a path from the root node to a leaf node in the tree, the architecture of each CNN node becomes shallower to take in smaller training patches. Our C K+1 DMF significantly (p<0.05) outperformed several conventional and ensemble CNN architectures, including conventional CNN (improvement by 10.3%) and CNN-based DMT (improvement by 5%).

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Cross-View Self-Similarity Using Shared Dictionary Learning for Cervical Cancer Staging

Cited by 10 publications

References 40 publications

Optimized Machine Learning based Classifications of Staging in Gynecological Cancers using Feature Subset through Fused Feature Selection Process

Optimized Machine Learning based Classifications of Staging in Gynecological Cancers using Feature Subset through Fused Feature Selection Process

Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging

Dynamic Multi-scale CNN Forest Learning for Automatic Cervical Cancer Segmentation

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