Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement

Teare, Philip; Fishman, Michael; Benzaquen, Oshra; Toledano, Eyal; Elnekave, Eldad

doi:10.1007/s10278-017-9993-2

Cited by 91 publications

(57 citation statements)

References 22 publications

(33 reference statements)

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“…In such medical domains, digital images are captured and provided to DL algorithms for Computer-Aided Diagnosis (CAD). These advance algorithms have already made their mark on automated detection of tuberculosis [5], breast malignancy [6], glaucoma [7], diabetic retinopathy [8] and serious brain findings such as stroke, haemorrhage, and mass effects [9].…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks

et al. 2020

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Propensity of skin diseases to manifest in a variety of forms, lack and maldistribution of qualified dermatologists, and exigency of timely and accurate diagnosis call for automated Computer-Aided Diagnosis (CAD). This study aims at extending previous works on CAD for dermatology by exploring the potential of Deep Learning to classify hundreds of skin diseases, improving classification performance, and utilizing disease taxonomy. We trained state-of-the-art Deep Neural Networks on two of the largest publicly available skin image datasets, namely DermNet and ISIC Archive, and also leveraged disease taxonomy, where available, to improve classification performance of these models. On DermNet we establish new state-of-the-art with 80% accuracy and 98% Area Under the Curve (AUC) for classification of 23 diseases. We also set precedence for classifying all 622 unique sub-classes in this dataset and achieved 67% accuracy and 98% AUC. On ISIC Archive we classified all 7 diseases with 93% average accuracy and 99% AUC. This study shows that Deep Learning has great potential to classify a vast array of skin diseases with near-human accuracy and far better reproducibility. It can have a promising role in practical real-time skin disease diagnosis by assisting physicians in large-scale screening using clinical or dermoscopic images.

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Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks

et al. 2020

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“…CNNs have been used in several medical imaging settings. In a nice example, CNNs have been used in conjunction with image preprocessing and random forests in an automated system to detect malignancy on mammograms (22). A sequence of imageprocessing steps optimized using genetic algorithms was applied (Fig.…”

Section: Anns (Fig 4)mentioning

confidence: 99%

Machine Learning in Nuclear Medicine: Part 1—Introduction

Uribe¹,

Mathotaarachchi

Gaudet

et al. 2019

J Nucl Med

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Learning Objectives: On successful completion of this activity, participants should be able to (1) provide an introduction to machine learning, neural networks, and deep learning; (2) discuss common machine learning algorithms with illustrative examples and figures; and (3) compare machine learning algorithms and provide guidance on selection for a given application. Financial Disclosure: Sandra E. Black received in-kind funding to her institution from GE Healthcare and Avid Pharmaceuticals. The authors of this article have indicated no other relevant relationships that could be perceived as a real or apparent conflict of interest. CME Credit: SNMMI is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to sponsor continuing education for physicians. SNMMI designates each JNM continuing education article for a maximum of 2.0 AMA PRA Category 1 Credits. Physicians should claim only credit commensurate with the extent of their participation in the activity. For CE credit, SAM, and other credit types, participants can access this activity through the SNMMI website (http://www.snmmilearningcenter.org) through April 2022. This article, the first in a 2-part series, provides an introduction to machine learning (ML) in a nuclear medicine context. This part addresses the history of ML and describes common algorithms, with illustrations of when they can be helpful in nuclear medicine. Part 2 focuses on current contributions of ML to our field, addresses future expectations and limitations, and provides a critical appraisal of what ML can and cannot do.

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“…Their model was pre-trained on ImageNet (a tactic regularly employed by practitioners) and yielded significant improvements in mass and microcalcification detection. In 2017, Teare et al [23] proposed a DL system that achieved a malignancy specificity of 80% at a sensitivity of 91% on DDSM, and their own proprietary dataset (which contained an equal number of malignant and non-malignant cases). Geras et al developed a DL system capable of classifying screening cases into BI-RADS 0, 1 or 2 [24].…”

Section: The Promise Of Deep Learningmentioning

confidence: 99%

The Role of Deep Learning in Breast Screening

Harvey

Karpati

Khara

et al. 2019

Curr Breast Cancer Rep

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Purpose of Review To review research on deep learning models and their potential application within breast screening. Recent Findings The greatest issue in breast screening is a workforce crisis across the UK, much of Europe and even Japan. Traditional computer-aided detection (CAD) for mammography decision-support could not reach the level of an independent reader. Deep learning (DL) outperforms CAD and is close to surpassing human performance. DL is already capable of decision support and density assessment for 2D full-field digital mammography (FFDM), and is on the cusp of providing consistent, accurate and interpretable mammography reading as an independent reader. Summary A bold vision for the future of breast cancer screening is required if programmes are to maintain double reading standards. DL provides the potential for single reading programmes, such as in the USA, to reach EU double reading accuracy, as well as providing practical support for adoption of the emerging modality of digital breast tomosynthesis.

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Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement

Cited by 91 publications

References 22 publications

Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks

Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks

Machine Learning in Nuclear Medicine: Part 1—Introduction

The Role of Deep Learning in Breast Screening

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