Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images

Zheng, Yuanjie; Wei, Benzheng; Liu, Hui; Xiao, Rui; Gee, James C.

doi:10.1016/j.compmedimag.2015.05.004

Cited by 8 publications

(3 citation statements)

References 47 publications

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“…There are three main reasons: (1) professional background and rich experience of pathologists are so difficult to inherit or innovate that primary-level hospitals and clinics suffer from the absence of skilled pathologists, (2) the tedious task is expensive and time-consuming, and (3) over fatigue of pathologists might lead to misdiagnosis. Hence, it is extremely urgent and important for the use of computer-aided breast cancer multi-classification, which can reduce the heavy workloads of pathologists and help avoid misdiagnosis 4–6 .…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model

Han¹,

Wei²,

Zheng³

et al. 2017

Sci Rep

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484

258

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Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.

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Section: Introductionmentioning

confidence: 99%

Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model

Han¹,

Wei²,

Zheng³

et al. 2017

Sci Rep

Self Cite

484

258

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“…MRI is subjected to imaging irregularities in the form of motion artifacts, noise, etc., which obstructs the segmentation process and ROI detection results are less accurate. In order to get accurate outputs, image registration positively impacts by providing non-linear ordered images and a crucial step in Dynamic Contrast-Enhanced MRI (DCE-MRI) based breast tumor diagnosis 15 . Apart from classical image segmentation algorithms, a number of machine learning (ML) techniques have contributed to automating the segmentation process with minimum human input, some of which are mentioned in the Related Work section and are helpful for the determination of cancer lesions.…”

Section: Introductionmentioning

confidence: 99%

Lesion detection in women breast’s dynamic contrast-enhanced magnetic resonance imaging using deep learning

Saikia,

Si,

Deb

et al. 2023

Sci Rep

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Breast cancer is one of the most common cancers in women and the second foremost cause of cancer death in women after lung cancer. Recent technological advances in breast cancer treatment offer hope to millions of women in the world. Segmentation of the breast’s Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is one of the necessary tasks in the diagnosis and detection of breast cancer. Currently, a popular deep learning model, U-Net is extensively used in biomedical image segmentation. This article aims to advance the state of the art and conduct a more in-depth analysis with a focus on the use of various U-Net models in lesion detection in women’s breast DCE-MRI. In this article, we perform an empirical study of the effectiveness and efficiency of U-Net and its derived deep learning models including ResUNet, Dense UNet, DUNet, Attention U-Net, UNet++, MultiResUNet, RAUNet, Inception U-Net and U-Net GAN for lesion detection in breast DCE-MRI. All the models are applied to the benchmarked 100 Sagittal T2-Weighted fat-suppressed DCE-MRI slices of 20 patients and their performance is compared. Also, a comparative study has been conducted with V-Net, W-Net, and DeepLabV3+. Non-parametric statistical test Wilcoxon Signed Rank Test is used to analyze the significance of the quantitative results. Furthermore, Multi-Criteria Decision Analysis (MCDA) is used to evaluate overall performance focused on accuracy, precision, sensitivity, F$$_1$$ 1 -score, specificity, Geometric-Mean, DSC, and false-positive rate. The RAUNet segmentation model achieved a high accuracy of 99.76%, sensitivity of 85.04%, precision of 90.21%, and Dice Similarity Coefficient (DSC) of 85.04% whereas ResNet achieved 99.62% accuracy, 62.26% sensitivity, 99.56% precision, and 72.86% DSC. ResUNet is found to be the most effective model based on MCDA. On the other hand, U-Net GAN takes the least computational time to perform the segmentation task. Both quantitative and qualitative results demonstrate that the ResNet model performs better than other models in segmenting the images and lesion detection, though computational time in achieving the objectives varies.

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“…So, there are 3 primary causes for this: (i) Pathologist' specialized backgrounds and extensive expertise have become so challenging to acquire or invent those basic clinics and hospitals lack a lot of qualified pathologists; (ii) the laborious work is costly and complex; and (iii) pathologists' tiredness may lead to misdiagnosed. As a result, it is critical to employ computer-aided breast cancer multiclassification, that can decrease pathologist' high responsibilities and assist minimize misdiagnosis [15]- [17].…”

mentioning

confidence: 99%

Breast cancer histological images nuclei segmentation and optimized classification with deep learning

Khan

Abbasi

Khurram

et al. 2022

IJECE

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Breast cancer incidences have grown worldwide during the previous few years. The histological images obtained from a biopsy of breast tissues are regarded as being the highest accurate approach to determine whether any cells exhibit symptoms of cancer. The visible position of nuclei inside the image is achieved through the use of instance segmentation, nevertheless, this work involves nucleus segmentation and features classification of the predicted nucleus for the achievement of best accuracy. The extracted features map using the feature pyramid network has been modified using segmenting objects by locations (SOLO) convolution with grasshopper optimization for multiclass classification. A breast cancer multiclassification technique based on a suggested deep learning algorithm was examined to achieve the accuracy of 99.2% using a huge database of ICIAR 2018, demonstrating the method’s efficacy in offering an important weapon for breast cancer multi-classification in a medical setting. The segmentation accuracy achieved is 88.46%.

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Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images

Cited by 8 publications

References 47 publications

Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model

Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model

Lesion detection in women breast’s dynamic contrast-enhanced magnetic resonance imaging using deep learning

Breast cancer histological images nuclei segmentation and optimized classification with deep learning

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