Is the future of breast imaging with AI?

Fuchsjäger, Michael

doi:10.1007/s00330-019-06286-6

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…AI helps the radiologists recognize their virtues and constraints, and eventually, deliver the best patient care. 33,34,47,56,58 Standardized views, availability of images for comparison, systematized reporting format, and classifiable outcome constitute the desirable properties that make screening mammography suitable to train AI algorithms. It may help take a second opinion, generate auto reports for typical cases, predict the likelihood of malignancy, and triage the patients.…”

Section: Advantages Of Aimentioning

confidence: 99%

An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging

Goyal¹

2021

RMI

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This review article aims to discuss current trends, techniques, and promising uses of artificial intelligence (AI) in breast imaging, apart from the pitfalls that may hinder its progress. It includes only the commonly used and basic terminology imperative for physicians to know. AI is not just a computerized approach but an interface between humans and machines. Apart from reducing workload and improved diagnostic accuracy, radiologists get more time for patient care or clinical work by using various machine learning techniques that augment their productivity. Inadequate data input with suboptimal pattern recognition, data extraction challenges, legal implications, and exorbitant costs are a few pitfalls that AI algorithms still face while analyzing and giving appropriate outcomes. Various machine learning approaches are used to construct prediction models for clinical decision support and ameliorating patient management. Since AI is still in its fledgling state, with many limitations for clinical implementation, clinical support and feedback are needed to avoid algorithmic errors. Hence, both machine learning and human insight complement each other in revolutionizing breast imaging.

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Section: Advantages Of Aimentioning

confidence: 99%

An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging

Goyal¹

2021

RMI

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“…Such CAD tools are intended to assist the radiologist during image interpretation by providing detection guidance and evaluating the likelihood of cancer, aiming to reduce interpretation time. Although a general adoption is yet to come ( 8 , 12 ), there is a trend toward a larger acceptance of CAD software as a helper tool in clinical practice ( 13 , 14 ). Today, common breast cancer CADx solutions provide a cancer-probability- or risk-based score of malignancy.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Multi-Task Learning to Cope With Poor and Missing Labels of Mammograms

Tardy¹,

Mateus²

2022

Front. Radio

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In breast cancer screening, binary classification of mammograms is a common task aiming to determine whether a case is malignant or benign. A Computer-Aided Diagnosis (CADx) system based on a trainable classifier requires clean data and labels coming from a confirmed diagnosis. Unfortunately, such labels are not easy to obtain in clinical practice, since the histopathological reports of biopsy may not be available alongside mammograms, while normal cases may not have an explicit follow-up confirmation. Such ambiguities result either in reducing the number of samples eligible for training or in a label uncertainty that may decrease the performances. In this work, we maximize the number of samples for training relying on multi-task learning. We design a deep-neural-network-based classifier yielding multiple outputs in one forward pass. The predicted classes include binary malignancy, cancer probability estimation, breast density, and image laterality. Since few samples have all classes available and confirmed, we propose to introduce the uncertainty related to the classes as a per-sample weight during training. Such weighting prevents updating the network's parameters when training on uncertain or missing labels. We evaluate our approach on the public INBreast and private datasets, showing statistically significant improvements compared to baseline and independent state-of-the-art approaches. Moreover, we use mammograms from Susan G. Komen Tissue Bank for fine-tuning, further demonstrating the ability to improve the performances in our multi-task learning setup from raw clinical data. We achieved the binary classification performance of AUC = 80.46 on our private dataset and AUC = 85.23 on the INBreast dataset.

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“…3,4 It is estimated that 100 million screening mammograms are performed each year. 5 Although there is ongoing debate about the utility of population-based screening practices for all subgroups of women, routine breast mammography has been shown to significantly reduce cancer-related mortality. 6 A study by a British group demonstrated that the absolute mortality reduction was 20%.…”

Section: Introductionmentioning

confidence: 99%

Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence

Tran

Sadeghi‐Naini

et al. 2020

Can Assoc Radiol J

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Breast cancer screening has been shown to significantly reduce mortality in women. The increased utilization of screening examinations has led to growing demands for rapid and accurate diagnostic reporting. In modern breast imaging centers, full-field digital mammography (FFDM) has replaced traditional analog mammography, and this has opened new opportunities for developing computational frameworks to automate detection and diagnosis. Artificial intelligence (AI), and its subdomain of deep learning, is showing promising results and improvements on diagnostic accuracy, compared to previous computer-based methods, known as computer-aided detection and diagnosis. In this commentary, we review the current status of computational radiology, with a focus on deep neural networks used in breast cancer screening and diagnosis. Recent studies are developing a new generation of computer-aided detection and diagnosis systems, as well as leveraging AI-driven tools to efficiently interpret digital mammograms, and breast tomosynthesis imaging. The use of AI in computational radiology necessitates transparency and rigorous testing. However, the overall impact of AI to radiology workflows will potentially yield more efficient and standardized processes as well as improve the level of care to patients with high diagnostic accuracy.

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Is the future of breast imaging with AI?

Cited by 7 publications

References 10 publications

An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging

An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging

Leveraging Multi-Task Learning to Cope With Poor and Missing Labels of Mammograms

Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence

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