Mr. Jason Su Profile

Jason Su

at Whiterabbit.ai

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Publications (3)

Proceedings Article | 4 April 2022 Presentation + Paper

WRDet: a breast cancer detector for full-field digital mammograms

Yen Nhi Truong Vu, Brent Mombourquette, Thomas Paul Matthews, Jason Su, Sadanand Singh

Proceedings Volume 12033, 120330V (2022) https://doi.org/10.1117/12.2611932

KEYWORDS: Mammography, Breast, Breast cancer, Data modeling, Computer aided diagnosis and therapy, Cancer

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Regular breast screening with mammography allows for early detection of cancer and reduces breast cancer mortality. However, significant false positive and false negative rates leave opportunities for improving diagnostic accuracy. Computer-aided detection (CAD) softwares have been available to radiologists for decades to address these issues. However, traditional CAD products have failed to improve interpretation of full-field digital mammography (FFDM) images in clinical practice due to low sensitivity and a large number of false positives per image. Usage of deep learning models have shown promise in improving performance of radiologists. Unfortunately, they still have a large amount of false positives per images at reasonable sensitivities. In this work, we propose a simple and intuitive two-stage detection framework, named WRDet. WRDet consists of two stages: a region proposal network that has been optimized to enhance sensitivity and a second-stage patch classifier that boosts specificity. We highlight different rules for matching predicted proposals and ground truth boxes that are commonly used in the mammography CAD literature and compare these rules in light of the high variability in quality of ground truth annotations of mammography datasets. We additionally propose a new criterion to match predicted proposals with loose bounding box annotations that is useful for two-stage CAD systems like WRDet. Using the common CAD matching criterion that considers a prediction true positive if its center falls within the ground truth annotation, our system achieves an overall sensitivity of 81.3% and 89.4% at 0.25 and 1 false positive mark per image, respectively. For the task of mass detection, we achieve a sensitivity of 85.3% and 92% at 0.25 and 1 false positive mark per image, respectively. We also compare our results with select models reported in literature using different matching criteria. Our results demonstrate the possibility of a CAD system that could be beneficial in improving accuracy of screening mammography worldwide.

Proceedings Article | 15 February 2021 Presentation + Paper

An improved mammography malignancy model with self-supervised learning

Yen Nhi Truong Vu, Trevor Tsue, Jason Su, Sadanand Singh

Proceedings Volume 11597, 115970W (2021) https://doi.org/10.1117/12.2582318

KEYWORDS: Mammography, Visual process modeling, Data modeling, Cancer, Tumor growth modeling, Statistical modeling, Reliability, Performance modeling, Receivers, Object recognition

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As deep learning greatly accelerates the field of computer vision, there has been growing interest in applying deep learning models for the purpose of predicting the presence of cancer in mammography images. However, unlike in conventional object recognition where one can leverage very large diverse datasets such as ImageNet, datasets for identifying cancer with mammography images are typically small and potentially non-representative due to the high cost of acquiring medical data and labels. This makes the training and assessment of such models challenging and raises reliability as well as generalizability concerns. In this work, we propose using the jigsaw task1 as a self-supervised method to pre-train models in the case where unlabeled data is available. We show that models that are pre-trained with this task outperform randomly initialized models even when they are only trained on a half or a quarter of the train set for the malignancy prediction task. In particular, we find that when using only a quarter of the labeled data, model trained using randomly initialized weights has an area under the receiver operating characteristic curve (AUC) of 0.944. On the other hand, the model that was pre-trained with the jigsaw task achieved an AUC of 0.958 when fine-tuned on the same quarter of the training set for the malignancy prediction task, outperforming even the model that was trained on all of the labeled data starting from randomized weights (0.954 AUC). Furthermore, we propose using performance on the jigsaw task as a way to measure confidence in our model’s predictions to enable the option to abstain from making a prediction when the model is not confident. We tested multiple strategies to filter out samples on which the jigsaw model perform poorly and measured the AUC in the remaining pool of samples. We show that the best filtering strategy improves malignancy prediction performance from an AUC of 0.890 on a completely unfiltered, off-site test set from a different country to an AUC of 0.913 on the filtered set.

Proceedings Article | 16 March 2020 Presentation + Paper

Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis

Sadanand Singh, Thomas Paul Matthews, Meet Shah, Brent Mombourquette, Trevor Tsue, Aaron Long, Ranya Almohsen, Stefano Pedemonte, Jason Su

Proceedings Volume 11314, 1131406 (2020) https://doi.org/10.1117/12.2549923

KEYWORDS: Digital breast tomosynthesis, Data modeling, Digital mammography, Mammography, Breast cancer

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