Mr. Jun Luo Profile

Jun Luo

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Proceedings Article | 4 April 2022 Presentation + Paper

Deep curriculum learning in task space for multi-class based mammography diagnosis

Jun Luo, Dooman Arefan, Margarita Zuley, Jules Sumkin, Shandong Wu

Proceedings Volume 12033, 120330C (2022) https://doi.org/10.1117/12.2612617

KEYWORDS: Mammography, Digital mammography, Medical research, Machine learning, Artificial intelligence, Medical imaging, Image classification

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Mammography is used as a standard screening procedure for the potential patients of breast cancer. Over the past decade, it has been shown that deep learning techniques have succeeded in reaching near-human performance in a number of tasks, and its application in mammography is one of the topics that medical researchers most concentrate on. In this work, we propose an end-to-end Curriculum Learning (CL) strategy in task space for classifying the three categories of Full-Field Digital Mammography (FFDM), namely Malignant, Negative, and False recall. Specifically, our method treats this three-class classification as a “harder” task in terms of CL, and creates an “easier” sub-task of classifying False recall against the combined group of Negative and Malignant. We introduce a loss scheduler to dynamically weight the contribution of the losses from the two tasks throughout the entire training process. We conduct experiments on an FFDM dataset of 1,709 images using 5-fold cross validation. The results show that our curriculum learning strategy can boost the performance for classifying the three categories of FFDM compared to the baseline strategies for model training.

Proceedings Article | 15 February 2021 Presentation + Paper

Medical knowledge-guided deep curriculum learning for elbow fracture diagnosis from x-ray images

Jun Luo, Gene Kitamura, Emine Doganay, Dooman Arefan, Shandong Wu

Proceedings Volume 11597, 1159712 (2021) https://doi.org/10.1117/12.2582184

KEYWORDS: X-rays, X-ray imaging, Medical imaging, Image classification, Data modeling, Bone, Diagnostics, Binary data

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Elbow fractures are one of the most common fracture types. Diagnoses on elbow fractures often need the help of radiographic imaging to be read and analyzed by a specialized radiologist with years of training. Thanks to the recent advances of deep learning, a model that can classify and detect different types of bone fractures needs only hours of training and has shown promising results. However, most existing deep learning models are purely data-driven, lacking incorporation of known domain knowledge from human experts. In this work, we propose a novel deep learning method to diagnose elbow fracture from elbow X-ray images by integrating domain-specific medical knowledge into a curriculum learning framework. In our method, the training data are permutated by sampling without replacement at the beginning of each training epoch. The sampling probability of each training sample is guided by a scoring criterion constructed based on clinically known knowledge from human experts, where the scoring indicates the diagnosis difficultness of different elbow fracture subtypes. We also propose an algorithm that updates the sampling probabilities at each epoch, which is applicable to other sampling-based curriculum learning frameworks. We design an experiment with 1865 elbow X-ray images for a fracture/normal binary classification task and compare our proposed method to a baseline method and a previous method using multiple metrics. Our results show that the proposed method achieves the highest classification performance. Also, our proposed probability update algorithm boosts the performance of the previous method.

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