Ms. Kadie Clancy Profile

Kadie Clancy

at Univ of Pittsburgh

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Proceedings Article | 15 February 2021 Presentation + Paper

Suspicion Map: an unsupervised middleware for analyzing digital mammograms

Kadie Clancy, Dooman Arefan, Long Gao, Shandong Wu

Proceedings Volume 11601, 116010N (2021) https://doi.org/10.1117/12.2582032

KEYWORDS: Mammography, Tumor growth modeling, Tissues, Breast, Machine learning, Image segmentation, Feature extraction, Digital imaging, Breast cancer

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Identifying “suspicious” regions is an essential process for clinical assessment of digital mammograms in breast cancer screening. Traditional solutions attempt to model malignant lesions directly, necessitating segmentations/annotations for training machine learning models. In this paper, we present a novel approach to identify a suspicion map – a middleware preserving only the suspicious regions in digital mammograms to effectively narrow down the image search space. Our unsupervised method is implemented by modeling normal breast tissue and subsequently identifying tissue abnormal to the model as suspicious. Our method consists of three main components: superpixel-based breast tissue patch generation, deep learning-based feature extraction from normal tissue patches, and breast density-guided one-class modeling of normal patches using the extracted features. Our machine learning approach is able to safely eliminate normal regions of tissue in a digital mammogram. Our normal tissue models were learned from 2,602 normal mammogram images and tested on 180 images (including 90 normal screening mammogram images and an independent set of 90 mammogram images with breast cancer diagnoses). Initial experiments showed that our proposed method can eliminate 97% of normal regions in the normal testing mammograms and 96% of normal regions in the malignant testing mammograms. Our method, based on modeling normal breast tissue, provides a novel and unsupervised scheme to more effectively analyze digital mammogram images towards identifying suspicious regions, and has the potential to benefit a variety of downstream applications for computeraided detection, diagnosis, and triage of breast cancer in mammogram images.

Proceedings Article | 13 March 2019 Paper

Deep learning for identifying breast cancer malignancy and false recalls: a robustness study on training strategy

Kadie Clancy, Lei Zhang, Aly Mohamed, Sarah Aboutalib, Wendie Berg, Shandong Wu

Proceedings Volume 10950, 1095005 (2019) https://doi.org/10.1117/12.2512942

KEYWORDS: Data modeling, Mammography, Breast, Breast cancer, Digital mammography, Image classification

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Identification of malignancy and false recalls (women who are recalled in screening for additional workup, but later proven benign) in screening mammography has significant clinical value for accurate diagnosis of breast cancer. Deep learning methods have recently shown success in the area of medical imaging classification. However, there are a multitude of different training strategies that can significantly impact the overall model performance for a specific classification task. In this study, we aimed to investigate the impact of training strategy on classification of digital mammograms by performing a robustness analysis of deep learning models to distinguish malignancy and false-recall from normal (benign) findings. Specifically, we employed several pre-training strategies including transfer learning with medical and non-medical datasets, layer freezing, and varied network structure on both binary and three-class classification tasks of digital mammography images. We found that, overall, deep learning models appear to be robust to some modifications of network structure and pre-training strategy that we tested for mammogram-specific classification tasks. However, for specific classification tasks, some training strategies offer performance gains. The most notable performance gains in our experiments involved residual network models.

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