A weakly supervised deep active contour model for nodule segmentation in thyroid ultrasound images

Li, Zhizhou; Zhou, Shichong; Chang, Cai; Wang, Yuanyuan; Guo, Yige

doi:10.1016/j.patrec.2022.12.015

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Koundal et al [ 18 ] introduced a method called “Spatial Neutrosophic Distance Regularized Level Set” (SNDRLS) for the identification of thyroid nodules. Li et al [ 19 , 20 ] proposed a deep active contour model for nodule segmentation. Li et al [ 19 , 20 ] introduced a Transformer and CNN-based method for the segmentation of malignant thyroid lesions.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images

Göreke

2023

Interdiscip Sci Comput Life Sci

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Nodules of thyroid cancer occur in the cells of the thyroid as benign or malign types. Thyroid sonographic images are mostly used for diagnosis of thyroid cancer. The aim of this study is to introduce a computer-aided diagnosis system that can classify the thyroid nodules with high accuracy using the data gathered from ultrasound images. Acquisition and labeling of sub-images were performed by a specialist physician. Then the number of these sub-images were increased using data augmentation methods. Deep features were obtained from the images using a pre-trained deep neural network. The dimensions of the features were reduced and features were improved. The improved features were combined with morphological and texture features. This feature group was rated by a value called similarity coefficient value which was obtained from a similarity coefficient generator module. The nodules were classified as benign or malignant using a multi-layer deep neural network with a pre-weighting layer designed with a novel approach. In this study, a novel multi-layer computer-aided diagnosis system was proposed for thyroid cancer detection. In the first layer of the system, a novel feature extraction method based on the class similarity of images was developed. In the second layer, a novel pre-weighting layer was proposed by modifying the genetic algorithm. The proposed system showed superior performance in different metrics compared to the literature. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1007/s12539-023-00560-4.

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Section: Introductionmentioning

confidence: 99%

“…Li et al [ 19 , 20 ] proposed a deep active contour model for nodule segmentation. Li et al [ 19 , 20 ] introduced a Transformer and CNN-based method for the segmentation of malignant thyroid lesions. Koundal et al [ 21 ] introduced a CAD system for segmentation of thyroid lesions on ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images

Göreke

2023

Interdiscip Sci Comput Life Sci

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“…Amid this landscape, this study embarked on a pioneering journey: introducing a multifaceted evaluation framework driven by multimodal federated learning. At its core, this framework employed a composite neural network model that synergistically integrates image data, harnessing the computational strengths of both the multilayer perceptron (MLP) and the convolutional neural network (CNN) [7][8][9][10]. This approach aimed to offer a more nuanced and accurate prediction of LNM.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT

Hu,

Ma,

Ding

et al. 2023

Cancers

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Gynecological malignancies, particularly lymph node metastasis, have presented a diagnostic challenge, even with traditional imaging techniques such as CT, MRI, and PET/CT. This study was conceived to explore and, subsequently, to bridge this diagnostic gap through a more holistic and innovative approach. By developing a comprehensive framework that integrates both non-image data and detailed MRI image analyses, this study harnessed the capabilities of a multimodal federated-learning model. Employing a composite neural network within a federated-learning environment, this study adeptly merged diverse data sources to enhance prediction accuracy. This was further complemented by a sophisticated deep convolutional neural network with an enhanced U-NET architecture for meticulous MRI image processing. Traditional imaging yielded sensitivities ranging from 32.63% to 57.69%. In contrast, the federated-learning model, without incorporating image data, achieved an impressive sensitivity of approximately 0.9231, which soared to 0.9412 with the integration of MRI data. Such advancements underscore the significant potential of this approach, suggesting that federated learning, especially when combined with MRI assessment data, can revolutionize lymph-node-metastasis detection in gynecological malignancies. This paves the way for more precise patient care, potentially transforming the current diagnostic paradigm and resulting in improved patient outcomes.

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Adversarial Keyword Extraction and Semantic-Spatial Feature Aggregation for Clinical Report Guided Thyroid Nodule Segmentation

Zhang,

Chen,

et al. 2023

Lecture Notes in Computer Science

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A weakly supervised deep active contour model for nodule segmentation in thyroid ultrasound images

Cited by 5 publications

References 23 publications

A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images

A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images

Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT

Adversarial Keyword Extraction and Semantic-Spatial Feature Aggregation for Clinical Report Guided Thyroid Nodule Segmentation

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