Mr. Ethan Tu Profile

Ethan Tu

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Publications (3)

SPIE Journal Paper | 4 June 2024

Near-pair patch generative adversarial network for data augmentation of focal pathology object detection models

Ethan Tu, Jonathan Burkow, Andy Tsai, Joseph Junewick, Francisco A. Perez, Jeffrey Otjen, Adam M. Alessio

JMI, Vol. 11, Issue 03, 034505, (June 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.3.034505

KEYWORDS: Education and training, Object detection, Radiography, Sensors, Data modeling, Pathology, Gallium nitride, Visualization, Machine learning, Feature extraction

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PurposeThe limited volume of medical training data remains one of the leading challenges for machine learning for diagnostic applications. Object detectors that identify and localize pathologies require training with a large volume of labeled images, which are often expensive and time-consuming to curate. To reduce this challenge, we present a method to support distant supervision of object detectors through generation of synthetic pathology-present labeled images.ApproachOur method employs the previously proposed cyclic generative adversarial network (cycleGAN) with two key innovations: (1) use of “near-pair” pathology-present regions and pathology-absent regions from similar locations in the same subject for training and (2) the addition of a realism metric (Fréchet inception distance) to the generator loss term. We trained and tested this method with 2800 fracture-present and 2800 fracture-absent image patches from 704 unique pediatric chest radiographs. The trained model was then used to generate synthetic pathology-present images with exact knowledge of location (labels) of the pathology. These synthetic images provided an augmented training set for an object detector.ResultsIn an observer study, four pediatric radiologists used a five-point Likert scale indicating the likelihood of a real fracture (1 = definitely not a fracture and 5 = definitely a fracture) to grade a set of real fracture-absent, real fracture-present, and synthetic fracture-present images. The real fracture-absent images scored 1.7±1.0, real fracture-present images 4.1±1.2, and synthetic fracture-present images 2.5±1.2. An object detector model (YOLOv5) trained on a mix of 500 real and 500 synthetic radiographs performed with a recall of 0.57±0.05 and an F2 score of 0.59±0.05. In comparison, when trained on only 500 real radiographs, the recall and F2 score were 0.49±0.06 and 0.53±0.06, respectively.ConclusionsOur proposed method generates visually realistic pathology and that provided improved object detector performance for the task of rib fracture detection.

Proceedings Article | 15 February 2021 Presentation + Paper

Machine learned approach for estimating myocardial blood flow from dynamic CT and coronary artery disease risk factors

Ethan Tu, Muneeza Azmat, Kelley Branch, Adam Alessio

Proceedings Volume 11600, 116001I (2021) https://doi.org/10.1117/12.2581703

KEYWORDS: Solid modeling, Positron emission tomography, Blood circulation, Arteries, Machine learning, Data modeling, Principal component analysis, Ischemia, Factor analysis, Signal attenuation

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Proceedings Article | 15 February 2021 Presentation + Paper

Machine learned versus analytical models for estimation of fractional flow reserve from CT-derived information

Muneeza Azmat, Ethan Tu, Kelley Branch, Adam Alessio

Proceedings Volume 11600, 116000W (2021) https://doi.org/10.1117/12.2581701

KEYWORDS: Data modeling, Statistical analysis, Machine learning, Blood circulation, Process modeling, General packet radio service, Error analysis, Arteries, Transducers, Performance modeling

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Fractional Flow Reserve (FFR) is a widely used metric to quantify the functional significance of stenoses in coronary arteries. FFR is the ratio of pressure before and after a stenosis and is measured using a transducer during coronary catheterization. To avoid unnecessary catheterization, many analytical and data-driven models based on non-invasive imaging have been proposed for FFR estimation. In this study, we construct physics-informed analytical models and datadriven machine learning models for FFR estimation based on CT-derived information. All four models require simple information about suspect stenoses, offering rapid, practical approaches for functional assessment. The four models we study are: (1) a patient-specific blood flow informed pressure drop model based on Navier-Stokes equations, (2) a purely geometric model based on stenosis area reduction, (3) a Gaussian process regression model trained on patient specific stenosis geometry and blood flow data, and (4) a Gaussian process regression model trained only on patient specific stenosis geometry. The models were developed and tested using a simulation study with ground truth FFR values from computational fluid dynamics analysis of blood flow through a population of stenosed arteries. In total, 60 different stenosis conditions based on known prevalence were simulated with a range of measurement errors leading to 10000 data sets. The RMSE of the model estimates for approach 1 through 4 are, 0.19, 0.45, 0.06, 0.14. The flow informed machine learning model leads to ~1% lower bias and ~12% lower variance than the flow informed analytical model. Considering the improved variance performance, the machine learning models likely outperform analytic expressions because they learn optimal regression associations robust to noise. This work suggests that machine learned approaches may be superior to conventional analytic expressions for FFR estimation, particularly when inputs contain realistic measurement error.

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