Deep classification of breast cancer in ultrasound images: more classes, better results with multi-task learning

Behboodi, Bahareh; Rasaee, Hamze; Tehrani, Ali K. Z.; Rivaz, Hassan

doi:10.1117/12.2581930

Cited by 5 publications

(4 citation statements)

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“…Study [ 32 ] introduced a multi-class classification strategy that incorporates background tissue as an additional class, resulting in improved invasive ductal carcinoma (IDC) detection. Further studies [ [36] , [37] , [38] , [39] ] introduced W-Net, UNet, and CNN models, demonstrating the diagnostic capability of RF data for breast tumor detection.…”

Section: Introductionmentioning

confidence: 99%

“…[ 40 ], a three-step image processing scheme was introduced to enhance the generalization of a deep learning model (VGG19) for breast cancer classification. The study showed improved performance on multiple datasets, emphasizing the importance of preprocessing in deep learning models for clinical applications [ [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] ].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

Norouzi Ghehi,

Fallah,

Rashidi

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

Norouzi Ghehi,

Fallah,

Rashidi

et al. 2024

Heliyon

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“…AI and deep learning approaches in medicine have a lot of potential, particularly in US diagnostic imaging, where large datasets must be managed. In US image analysis, many researchers have shown the promising results in detection of breast lesions [7]- [10], muscle [11], thyroid nodule [12], prostate [13], liver [14], brain [15]. However, due to limited studies on the automatic segmentation of uterus US images, the main focus of the current study is to investigate more on automatic segmentation of 3D uterus US images and to eliminate the need for manual initialization in the previous semi-automatic algorithms using the recent deep learning-based techniques.…”

Section: Introductionmentioning

confidence: 99%

Automatic 3D Ultrasound Segmentation of Uterus Using Deep Learning

Behboodi¹,

Rivaz²,

Lalondrelle³

et al. 2021

Preprint

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On-line segmentation of the uterus can aid effective image-based guidance for precise delivery of dose to the target tissue (the uterocervix) during cervix cancer radiotherapy. 3D ultrasound (US) can be used to image the uterus, however, finding the position of uterine boundary in US images is a challenging task due to large daily positional and shape changes in the uterus, large variation in bladder filling, and the limitations of 3D US images such as low resolution in the elevational direction and imaging aberrations. Previous studies on uterus segmentation mainly focused on developing semi-automatic algorithms where require manual initialization to be done by an expert clinician. Due to limited studies on the automatic 3D uterus segmentation, the aim of the current study was to overcome the need for manual initialization in the semi-automatic algorithms using the recent deep learning-based algorithms. Therefore, we developed 2D UNet-based networks that are trained based on two scenarios. In the first scenario, we trained 3 different networks on each plane (i.e., sagittal, coronal, axial) individually. In the second scenario, our proposed network was trained using all the planes of each 3D volume. Our proposed schematic can overcome the initial manual selection of previous semi-automatic algorithm.

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“…It was shown that training a network with more correlated tasks to predict multiple outputs results in a better performance compared to the same network assigned to only one task [16]. MTL was used to more reliably categorise breast cancer ultrasound images [23].…”

Section: Introductionmentioning

confidence: 99%

Explainable AI and susceptibility to adversarial attacks: a case study in classification of breast ultrasound images

Rasaee¹,

Rivaz²

2021

Preprint

Self Cite

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Ultrasound is a non-invasive imaging modality that can be conveniently used to classify suspicious breast nodules and potentially detect the onset of breast cancer. Recently, Convolutional Neural Networks (CNN) techniques have shown promising results in classifying ultrasound images of the breast into benign or malignant. However, CNN inference acts as a black-box model, and as such, its decision-making is not interpretable. Therefore, increasing effort has been dedicated to explaining this process, most notably through GRAD-CAM and other techniques that provide visual explanations into inner workings of CNNs. In addition to interpretation, these methods provide clinically important information, such as identifying the location for biopsy or treatment. In this work, we analyze how adversarial assaults that are practically undetectable may be devised to alter these importance maps dramatically. Furthermore, we will show that this change in the importance maps can come with or without altering the classification result, rendering them even harder to detect. As such, care must be taken when using these importance maps to shed light on the inner workings of deep learning. Finally, we utilize Multi-Task Learning (MTL) and propose a new network based on ResNet-50 to improve the classification accuracies. Our sensitivity and specificity is comparable to the state of the art results.

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Deep classification of breast cancer in ultrasound images: more classes, better results with multi-task learning

Cited by 5 publications

References 0 publications

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

Automatic 3D Ultrasound Segmentation of Uterus Using Deep Learning

Explainable AI and susceptibility to adversarial attacks: a case study in classification of breast ultrasound images

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