A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images

Chowdary, J. Lavanya; Yogarajah, Pratheepan; Chaurasia, Priyanka; Guruviah, Velmathi

doi:10.1177/01617346221075769

Cited by 33 publications

(22 citation statements)

References 53 publications

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“…We employ commonly-used BUS segmentation metrics [9], [15], [17], [18], [30], [31] including sensitivity (SEN), specificity (SPE), accuracy (ACC), dice similarity coefficient (DSC), and intersection over the union of tumor (tumor IoU) to quantitatively evaluate the segmentation performance. Higher values of these metrics represent better segmentation performance.…”

Section: B Performance Evaluationmentioning

confidence: 99%

“…For example, Zhou et al [29] propose an MTL framework with a light-weight multi-scale network to iteratively refine features to highlight tumor regions for better 3D BUS image classification. Chowdary et al [31] propose an MTL framework with a dense branch to combine multi-scale features from different layers of the network for efficient classification of BUS images. Zhang et al [30] propose an MTL framework with soft and hard attention mechanisms to guide the model to pay more attention to tumor regions to boost classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A Regional-Attentive Multi-Task Learning Framework for Breast Ultrasound Image Segmentation and Classification

Huang

2023

IEEE Access

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Breast ultrasound (BUS) imaging is commonly used in the early detection of breast cancer as a portable, valuable, and widely available diagnosis tool. Automated BUS image classification and segmentation can assist radiologists in making accurate and fast decisions. Recent studies illustrate that tumor, peritumoral, and background regions of BUS images provide valuable information for BUS image segmentation or classification. However, few studies have investigated the influence of these three regions on multi-task learning. In this study, we propose an RMTL-Net to simultaneously segment tumor regions and classify tumors in BUS images into benign or malignant categories. To improve both segmentation and classification performance, we design a regional attention (RA) module that employs the predicted probability maps to automatically guide the classifier to learn important category-sensitive information in the tumor, peritumoral, and background regions and seamlessly fuse them to obtain a better feature representation. We conduct detailed ablation experiments of the proposed RA module and comparative experiments with four recent state-of-the-art peer multi-task learning methods, three single-task segmentation methods, and four single-task classification methods on two public BUS datasets. Experimental results show that the proposed RMTL-Net achieves the best overall segmentation and classification accuracy in terms of five segmentation metrics and six classification metrics.

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Section: B Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Regional-Attentive Multi-Task Learning Framework for Breast Ultrasound Image Segmentation and Classification

Huang

2023

IEEE Access

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“…14 This shared design leverages semantic information to decode the lesion type and location simultaneously, which can reduce the risk of overfitting and improve learning efficiency and robustness. [14][15][16][17][18][19][20] Zhou et al employed an encoder-decoder network (VNet) for the segmentation task, while the intermediate feature maps were reused for classification by a lightweight network only consisting of a global average pooling layer and three fully-connected layers. 17 network.…”

Section: Introductionmentioning

confidence: 99%

“…17 network. 18 Some MTL methods simplify pixel-wise lesion segmentation to detection in the form of bounding boxes instead. 9,13 For instance, Cao et al studied and compared the performance of several popular object detection methods such as YOLO and SSD.…”

Section: Introductionmentioning

confidence: 99%

Joint localization and classification of breast tumors on ultrasound images using a novel auxiliary attention-based framework

Fan¹,

Gong²,

Tang³

et al. 2022

Preprint

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Automatic breast lesion detection and classification is an important task in computer-aided diagnosis, in which breast ultrasound (BUS) imaging is a common and frequently used screening tool. Recently, a number of deep learning-based methods have been proposed for joint localization and classification of breast lesions using BUS images. In these methods, features extracted by a shared network trunk are appended by two independent network branches to achieve classification and localization. Improper information sharing might cause conflicts in feature optimization in the two branches and leads to performance degradation. Also, these methods generally require large amounts of pixel-level annotated data for model training. To overcome these limitations, we proposed a novel joint localization and classification model based on the attention mechanism and disentangled semi-supervised learning strategy. The model used in this study is composed of a classification network and an auxiliary lesion-aware network. By use of the attention mechanism, the auxiliary lesion-aware network can optimize multi-scale intermediate feature maps and extract rich semantic information to improve classification and localization performance. The disentangled semi-supervised learning strategy only requires incomplete training datasets for model training. The proposed modularized framework allows flexible network replacement to be generalized for various applications. Experimental results on two different breast ultrasound image datasets

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“…One path hierarchically extracts features from the input image, and the other path focuses on yielding geometrical features of the image. With remarkable performance achieved by the U-shaped encoder-decoder networks in medical image segmentation [11]several studies employed such U-shaped networks for mass segmentation. For instance, Revitha et al [12] developed an encoder-decoder network with deep supervision for segmenting masses from mammograms.…”

Section: Introductionmentioning

confidence: 99%

EU-Net: Enhanced U-shaped Network for Breast Mass Segmentation

Chowdary¹,

Yogarajah²

2022

Preprint

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<p>Segmentation of breast masses in digital mammograms is very challenging due to its complexity. The recent U-shaped encoder-decoder networks achieved remarkable performance in medical image segmentation. However, these networks have some limitations: a) The multi-scale context information is required to accurately segment mass but is not effectively extracted and utilized. b) The global context information is often ignored by the skip connection. To overcome these limitations and achieve better segmentation, we propose an Enhanced U-shaped Network (EU-Net). The proposed EU-Net comprises of 3 novel components: 1) dense block, which is employed in the encoder and the decoder in place of convolutional layers to achieve the multi-scale features. 2) Multi-Scale Feature Extraction and Fusion, which is used in the junction between the encoder and the decoder for further extracting and fusing the multi-scale context information. 3) Skip Connection Reconstruction, which is inserted between the encoder and the decoder at each stage, to redesign the skip connection and emphasize the global context information. Extensive experimental results under different settings show that the proposed EU-Net achieves superior performances than the previous state-of-the-art segmentation models, and other existing approaches on IN-Breast and CBIS-DDSM mammogram datasets. The generalization ability of the proposed EU-Net is evidenced through crossdataset and ternary dataset evaluation performance. In the ternary dataset evaluation, the model is trained and evaluated on the UDIAT breast ultrasound dataset without finetuning. The EU-Net achieves higher generalization performance in both evaluation experiments. These experiments collectively indicate the efficiency and high generalization ability of the proposed EU-Net.</p>

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A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images

Cited by 33 publications

References 53 publications

A Regional-Attentive Multi-Task Learning Framework for Breast Ultrasound Image Segmentation and Classification

A Regional-Attentive Multi-Task Learning Framework for Breast Ultrasound Image Segmentation and Classification

Joint localization and classification of breast tumors on ultrasound images using a novel auxiliary attention-based framework

EU-Net: Enhanced U-shaped Network for Breast Mass Segmentation

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