Breast nodule classification with two-dimensional ultrasound using Mask-RCNN ensemble aggregation

Evain, Ewan; Raynaud, Caroline; Ciofolo-Veit, Cybèle; Popoff, Alexandre; Caramella, T.; Kbaier, P.; Balleyguier, Corinne; Harguem-Zayani, Sana; Dapvril, Héloïse; Ceugnart, L.; Monroc, M.; Chamming’s, Foucauld; Doutriaux-Dumoulin, I.; Thomassin‐Naggara, Isabelle; Haquin, Audrey; Charlot, M.; Orabona, Joseph; Fourquet, T.; Bousaid, Imad; Lassau, Nathalie; Olivier, Antoine

doi:10.1016/j.diii.2021.09.002

Cited by 13 publications

(5 citation statements)

References 23 publications

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“…An ANN-based DL algorithm allowed reliable differentiation of malignant versus nonmalignant breast nodules first assessed with breast ultrasound and initially reported as breast imaging reporting data systems (BI-RADS) 3 and 4. 32 Hamm et al developed ANN for automated characterization of liver nodules on magnetic resonance imaging (MRI), with a 92% sensitivity and 98% specificity in the training set and higher sensitivity and specificity compared to radiologists in the test dataset. 33 An ML model allowed to distinguish benign from malignant cystic renal lesions on CT with an AUC of 0.96 and a benefit in the clinical decision algorithm over management guidelines based on Bosniak classification.…”

Section: Risk Stratificationmentioning

confidence: 99%

Artificial Intelligence in the Era of Precision Oncological Imaging

Cellina

Cè

Khenkina

et al. 2022

Technol Cancer Res Treat

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Rapid-paced development and adaptability of artificial intelligence algorithms have secured their almost ubiquitous presence in the field of oncological imaging. Artificial intelligence models have been created for a variety of tasks, including risk stratification, automated detection, and segmentation of lesions, characterization, grading and staging, prediction of prognosis, and treatment response. Soon, artificial intelligence could become an essential part of every step of oncological workup and patient management. Integration of neural networks and deep learning into radiological artificial intelligence algorithms allow for extrapolating imaging features otherwise inaccessible to human operators and pave the way to truly personalized management of oncological patients. Although a significant proportion of currently available artificial intelligence solutions belong to basic and translational cancer imaging research, their progressive transfer to clinical routine is imminent, contributing to the development of a personalized approach in oncology. We thereby review the main applications of artificial intelligence in oncological imaging, describe the example of their successful integration into research and clinical practice, and highlight the challenges and future perspectives that will shape the field of oncological radiology.

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Section: Risk Stratificationmentioning

confidence: 99%

Artificial Intelligence in the Era of Precision Oncological Imaging

Cellina

Cè

Khenkina

et al. 2022

Technol Cancer Res Treat

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“…Each patch is treated as a separate ticket. The basic RGB values of the pixels are added together to make the feature set of each patch [23]. For a 4x4 patch, this gives the feature set a measure of 48.…”

Section: Methodsmentioning

confidence: 99%

Enhancing Breast Cancer Collagen Segmentation with Improved Swim Transformation Algorithm

Ritesh V. Patil

2024

jes

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Breast cancer is a major health problem around the world, and accurately separating collagen fibers in breast cancer cells is important for both detection and planning treatment. Because it can make the differences between fibers and the background stand out more, the Swim Transformation (SWT) algorithm has shown promise in separating collagen fibers. However, the original SWT algorithm has some flaws, such as being easily affected by noise and artifacts. We suggest a better version of the SWT algorithm in this study to fix these problems and make collagen segmentation in breast cancer tissues better. A new step in the preparation process was added to cut down on noise, and adaptable thresholding was added to make it easier to find collagen fibers. We also add a step called "post-processing" to get rid of any errors and make the segmentation results better. We used a collection of microscopic images of breast cancer tissues in tests to see how well the proposed method worked. For segmentation, we looked at how our better SWT algorithm did compared to the original SWT algorithm and other cutting-edge segmentation methods. Based on our data, the suggested method does a better job in terms of accuracy, sensitivity, and precision. In addition, we add a deep learning model for finding breast cancer that uses Long Short-Term Memory (LSTM) and Bidirectional LSTM models to our work. A set of images of breast cancer is used to teach a deep learning model that can tell from collagen segmentation results whether or not there are dangerous cells. The paper shown an improved SWT algorithm for separating collagen in breast cancer cells. This algorithm fixes the problems with the first one and gets better results for separating collagen. It's possible that the suggested formula will help doctors find and plan better treatments for breast cancer by making it easier to separate collagen fibers more accurately.

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“…For medical image analysis, the application of deep learning began and grew rapidly in 2015 and 2016 [12]. Deep learning has been applied in ultrasound, X-rays, computed tomography (CT) and magnetic resonance imaging [13][14][15][16][17][18]. Deep learning is now considered as the stateof-the-art method in medical image analysis.…”

Section: From Radiomics To Deep Learningmentioning

confidence: 99%

Artificial intelligence in lung cancer: current applications and perspectives

et al. 2022

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Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the development of AI and in particular deep learning. We have now entered a phase of adopting AI into clinical practice. The objective of this article was to review the current applications and perspectives of AI in thoracic oncology. For pulmonary nodule detection, computer-aided detection (CADe) tools have been commercially available since the early 2000s. The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe tools with fewer false-positive results per examination. Classical machine learning and deeplearning methods were also used for pulmonary nodule segmentation allowing nodule volumetry and pulmonary nodule characterization. For pulmonary nodule characterization, radiomics and deep-learning approaches were used. Data from the National Lung Cancer Screening Trial (NLST) allowed the development of several computer-aided diagnostic (CADx) tools for diagnosing lung cancer on chest computed tomography. Finally, AI has been used as a means to perform virtual biopsies and to predict response to treatment or survival. Thus, many detection, characterization and stratification tools have been proposed, some of which are commercially available.

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Breast nodule classification with two-dimensional ultrasound using Mask-RCNN ensemble aggregation

Cited by 13 publications

References 23 publications

Artificial Intelligence in the Era of Precision Oncological Imaging

Artificial Intelligence in the Era of Precision Oncological Imaging

Enhancing Breast Cancer Collagen Segmentation with Improved Swim Transformation Algorithm

Artificial intelligence in lung cancer: current applications and perspectives

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