Prof. Yelena Yesha Profile

Prof. Yelena Yesha

at Univ. of Miami

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Publications (3)

Proceedings Article | 3 April 2024 Presentation + Paper

Quantifying input data drift in medical machine learning models by detecting change-points in time-series data

Smriti Prathapan, Ravi Samala, Nathan Hadjiyski, Pierre-François D’Haese, Fabien Maldonado, Phuong Nguyen, Yelena Yesha, Berkman Sahiner

Proceedings Volume 12927, 129270E (2024) https://doi.org/10.1117/12.3008771

KEYWORDS: Data modeling, Artificial intelligence, Performance modeling, Mammography, Machine learning, Computer simulations, Tunable filters, Solid modeling, Education and training, Detection theory

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Proceedings Article | 4 April 2022 Presentation

Pairwise meta learning pipeline: classifying COVID-19 abnormalities on chest radio-graphs

Sourajit Saha, Yaacov Yesha, Yelena Yesha, Aryya Gangopadhyay, David Chapman, Michael Morris, Babak Saboury, Phuong Nguyen

Proceedings Volume PC12033, PC1203302 (2022) https://doi.org/10.1117/12.2613235

KEYWORDS: Chest, Computer aided diagnosis and therapy, Solid modeling, Medicine, Imaging informatics, Image classification, Convolutional neural networks, Computer aided design, Classification systems, CAD systems

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Proceedings Article | 16 March 2020 Paper

Active semi-supervised expectation maximization learning for lung cancer detection from Computerized Tomography (CT) images with minimally label training data

Phuong Nguyen, David Chapman, Sumeet Menon, Michael Morris, Yelena Yesha

Proceedings Volume 11314, 113142E (2020) https://doi.org/10.1117/12.2549655

KEYWORDS: Expectation maximization algorithms, Lung cancer, Computed tomography, Cancer, Computer aided diagnosis and therapy, Radiology, Artificial intelligence

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Artificial intelligence (AI) has great potential in medical imaging to augment the clinician as a virtual radiology assistant (vRA) through enriching information and providing clinical decision support. Deep learning is a type of AI that has shown promise in performance for Computer Aided Diagnosis (CAD) tasks. A current barrier to implementing deep learning for clinical CAD tasks in radiology is that it requires a training set to be representative and as large as possible in order to generalize appropriately and achieve high accuracy predictions. There is a lack of available, reliable, discretized and annotated labels for computer vision research in radiology despite the abundance of diagnostic imaging examinations performed in routine clinical practice. Furthermore, the process to create reliable labels is tedious, time consuming and requires expertise in clinical radiology. We present an Active Semi-supervised Expectation Maximization (ASEM) learning model for training a Convolutional Neural Network (CNN) for lung cancer screening using Computed Tomography (CT) imaging examinations. Our learning model is novel since it combines Semi-supervised learning via the Expectation-Maximization (EM) algorithm with Active learning via Bayesian experimental design for use with 3D CNNs for lung cancer screening. ASEM simultaneously infers image labels as a latent variable, while predicting which images, if additionally labeled, are likely to improve classification accuracy. The performance of this model has been evaluated using three publicly available chest CT datasets: Kaggle2017, NLST, and LIDC-IDRI. Our experiments showed that ASEM-CAD can identify suspicious lung nodules and detect lung cancer cases with an accuracy of 92% (Kaggle17), 93% (NLST), and 73% (LIDC) and Area Under Curve (AUC) of 0.94 (Kaggle), 0.88 (NLST), and 0.81 (LIDC). These performance numbers are comparable to fully supervised training, but use only slightly more than 50% of the training data labels .

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