Deep Learning in Breast Cancer Screening

Harvey, Hugh; Heindl, Andreas; Khara, Galvin; Korkinof, Dimitrios; O’Neill, Michael; Yearsley, Joseph; Karpati, Edith; Rijken, Tobias; Kecskemethy, Peter D.; Forrai, Gábor

doi:10.1007/978-3-319-94878-2_14

Cited by 11 publications

(7 citation statements)

References 92 publications

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“…Image perception of medical image data are relatively complex compared with nonmedical image perception tasks. Most convolutional neural networks for classification of images are trained and tested on two-dimensional images with fewer than 300 × 300 pixels (34). Medical images, however, exceed these dimensions; the in-plane spatial resolution is generally higher than 300 × 300 pixels, and many medical image studies are threedimensional instead of two-dimensional.…”

Section: Resampling Medical Imagesmentioning

confidence: 99%

Preparing Medical Imaging Data for Machine Learning

Willemink¹,

Koszek²,

Hardell³

et al. 2020

Radiology

Self Cite

664

364

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Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability.

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Section: Resampling Medical Imagesmentioning

confidence: 99%

Preparing Medical Imaging Data for Machine Learning

Willemink¹,

Koszek²,

Hardell³

et al. 2020

Radiology

Self Cite

664

364

View full text Add to dashboard Cite

show abstract

“…IDTechEx, a wellknown British research company, predicts that the market for image-based artificial intelligence medical diagnosis will grow by nearly 10000% by 2040. So far, deep learning has been widely used in the diagnosis of many diseases, such as breast cancer screening, benign and malignant thyroid nodules, and lung cancer detection [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Segmentation of Ovarian Cancer Using a Multitask Model in Pelvic CT Images

Wang

Zheng

2022

Oxidative Medicine and Cellular Longevity

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Ovarian cancer is one of the most common malignant tumours of female reproductive organs in the world. The pelvic CT scan is a common examination method used for the screening of ovarian cancer, which shows the advantages in safety, efficiency, and providing high-resolution images. Recently, deep learning applications in medical imaging attract more and more attention in the research field of tumour diagnostics. However, due to the limited number of relevant datasets and reliable deep learning models, it remains a challenging problem to detect ovarian tumours on CT images. In this work, we first collected CT images of 223 ovarian cancer patients in the Affiliated Hospital of Qingdao University. A new end-to-end network based on YOLOv5 is proposed, namely, YOLO-OCv2 (ovarian cancer). We improved the previous work YOLO-OC firstly, including balanced mosaic data enhancement and decoupled detection head. Then, based on the detection model, a multitask model is proposed, which can simultaneously complete the detection and segmentation tasks.

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“…Further, they need significant computational resources. For instance, a DNN-based system for breast cancer screening can provide much more effective, efficient, and patient-centric breast cancer screening support than ever before [29]. However, some small clinics may not be able to train a breast cancer screening ML model based on their collected patients' healthcare records because of a lack of adequate computing power and ML expertise.…”

Section: Motivationmentioning

confidence: 99%

NN-EMD: Efficiently Training Neural Networks using Encrypted Multi-Sourced Datasets

Joshi

2020

Preprint

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Training complex neural network models using third-party cloud-based infrastructure among multiple data sources becomes a promising approach in existing machine learning solutions. However, privacy concerns of large-scale data collection and recent regulations or acts have restricted the availability and use of privacy sensitive data in the third-party infrastructure. To address the privacy issue, a promising emerging approach is to train a neural network model over an encrypted dataset. Specifically, the model training process can be outsourced to a third party such as a cloud service that is backed by significant computing power, while the encrypted training data keeps the data confidential from the third party. Compared to training a traditional machine learning model over encrypted data, however, it is extremely challenging to train a deep neural network (DNN) model over encrypted data for two reasons: first, it requires large-scale computation over huge datasets; second, the existing solutions for computation over encrypted data, such as using homomorphic encryption, is inefficient. Further, for enhanced performance of a DNN model, we also need to use huge training datasets composed of data from multiple data sources that may not have pre-established trust relationships among each other. We propose a novel framework, NN-EMD, to train DNN over multiple encrypted datasets collected from multiple sources. Toward this, we propose a set of secure computation protocols using hybrid functional encryption schemes. We evaluate our framework for performance with regards to the training time and model accuracy on the MNIST datasets. We show that compared to other existing frameworks, our proposed NN-EMD framework can significantly reduce the training time, while providing comparable model accuracy and privacy guarantees as well as supporting multiple data sources. Furthermore, the depth and complexity of neural networks do not affect the training time despite introducing a privacy-preserving NN-EMD setting.Preprint. Under review.

show abstract

Deep Learning in Breast Cancer Screening

Cited by 11 publications

References 92 publications

Preparing Medical Imaging Data for Machine Learning

Preparing Medical Imaging Data for Machine Learning

Automatic Detection and Segmentation of Ovarian Cancer Using a Multitask Model in Pelvic CT Images

NN-EMD: Efficiently Training Neural Networks using Encrypted Multi-Sourced Datasets

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