Detection and characterization of MRI breast lesions using deep learning

Hérent, Paul; Schmauch, Benoît; Jehanno, Paul; Dehaene, Olivier; Saillard, Charlie; Balleyguier, Corinne; Arfi-Rouche, Julia; Jégou, Simon

doi:10.1016/j.diii.2019.02.008

Cited by 95 publications

(63 citation statements)

References 16 publications

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“…This result suggests that our AI system may be useful not only for beginners but also for experts. Similar to our results, other studies have reported the utility of DL for breast MRI [15][16][17][18]. Hernet et al developed a supervised-attention model with DL based on ResNet-50 that was trained to detect and characterize lesions using a single two-dimensional T1-weighted fat-suppressed contrast-enhanced MRIs of 335 cases.…”

Section: Discussionsupporting

confidence: 88%

“…Although some reports have built DL architecture to automatically extract features of images learned from MRI data and have evaluated an architecture to diagnose breast lesions [15][16][17][18][19], to our knowledge there is no report evaluating both AI and human interpretations of object detection on maximum intensity projections (MIPs) of DCE breast MRI. Because evidence of the usefulness of AI for object detection in breast MRI is inadequate, verification of its clinical utility is needed.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

et al. 2020

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We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

et al. 2020

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“…As a result, the pre-trained CNN to assess the required level achieves performance improvements in plantar pressure image dataset, and the classification was also incrementally fine-tuned. Detection and characterization of plantar pressure image data-set by using deep learning with indices of Training-Testing Data Splitting (TTDS), Class Type (CT), Area Under the Curve (AUC), Average Precision Score (APS), recall, precision, f1 score (an index used to measure the accuracy of binary classification model in statistics -it considers both the accuracy and recall of the classification model), N, UN, Ave are compared and summarized in Table I [ 37], [40]- [42]. Table II shows the details of the public test database of SUN, CUB, AWA1, WAW2, and aPY [37].…”

Section: Discussionmentioning

confidence: 99%

Learning Models for Semantic Classification of Insufficient Plantar Pressure Images

Wu¹,

Wu²,

Dey³

et al. 2020

IJIMAI

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Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation). The proposed method for the plantar pressure classification task shows high performance in most indices when comparing with other methods. The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields.

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“…It will play an essential role in all steps of mammography and digital breast tomosynthesis from image creation and deionizing to risk assessment, cancer detection, and finally, therapy selection and response prediction [6]. AI has a significant role in the interpretation of breast cancer; Herent et al demonstrated the capability of a deep learning model to discriminate between benign and malignant lesions using magnetic resonance imaging (MRI) and identify various histological subtypes of breast cancer [7]. AI has multiple implications in thoracic imaging such as lung nodule assessment, tuberculosis or pneumonia detection, or estimation of diffuse lung diseases.…”

Section: Introductionmentioning

confidence: 99%

Is Artificial Intelligence the New Friend for Radiologists? A Review Article

Gampala¹,

Vankeshwaram²,

Gadula³

2020

Cureus

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Artificial intelligence (AI) is a path-breaking advancement for many industries, including the health care sector. The expeditious development of information technology and data processing has led to the formation of recent tools known as artificial intelligence. Radiology has been a portal for medical technological advancements, and AI will likely be no dissimilar. Radiology is the platform for many technological advances in the medical field; AI can undoubtedly impact every step of a radiologist's workflow. AI can simplify every activity like ordering and scheduling, protocoling and acquisition, image interpretation, reporting, communication, and billing. AI has eminent potential to augment efficiency and accuracy throughout radiology, but it also possesses inherent drawbacks and biases. We collected studies that were published in the past five years using PubMed as our database. We chose studies that were relevant to artificial intelligence in radiology. We mainly focused on the overview of AI in radiology, components included in the functioning of AI, AI assisting in the radiologists' workflow, ethical aspects of AI, challenges, and biases that AI experiencing together with some clinical applications of AI. Of all 33 studies, we found 15 articles discussed the overview and components of AI, five articles about AI affecting radiologist's workflow, five articles related to challenges and biases in AI, two articles discussed ethical aspects of AI, and six articles about practical implications of AI. We found out that the application of AI could make time-dependent tasks that can be performed effortlessly, permitting radiologists more time and opportunities to engage in patient care via increased time for consultation and development in imaging and extracting useful data from those images. AI could only be an aid to radiologists but will not replace a radiologist. Radiologists who use AI to their benefit, rather than to avoid it out of fear, might supersede those radiologists who do not. Substantial research should be done regarding the practical implications of AI algorithms for residents curriculum and the benefits of AI in radiology.

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Detection and characterization of MRI breast lesions using deep learning

Cited by 95 publications

References 16 publications

Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Learning Models for Semantic Classification of Insufficient Plantar Pressure Images

Is Artificial Intelligence the New Friend for Radiologists? A Review Article

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