Mr. Jaeil Kim Profile

Jaeil Kim

at Kyungpook National Univ

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Proceedings Article | 16 March 2020 Presentation + Paper

Weakly-supervised US breast tumor characterization and localization with a box convolution network

Chanho Kim, Won Hwa Kim, Hye Jung Kim, Jaeil Kim

Proceedings Volume 11314, 1131419 (2020) https://doi.org/10.1117/12.2549203

KEYWORDS: Convolution, Tumors, Breast, Diagnostics, Breast cancer, Ultrasonography, Radiology, Image classification

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In US breast tumor diagnosis, machine learning approaches for the malignancy classification and the mass localization have been attracting many researchers to improve the diagnostic sensitivity and specificity while reducing the image interpretation time. Recently, fully-supervised deep learning methods showed their promising results in those tasks. However, the full supervision for the localization requires human efforts and time to annotate ground truth regions. In this paper, we present a weakly-supervised deep network which can localize breast masses in US images from only diagnostic labels (i.e., malignant and benign). Specifically, we exploit a flexible convolution method, which learns the size and offset of the convolution kernel, in the classification network to detect more relevant regions of breast masses against their various size and shape. Experimental results show that the proposed network outperform conventional CNN models, such as VGG-16 and VGG-16 with dilated convolution. The proposed model achieved 89.03% in the binary classification accuracy. To evaluate the localization performance with weakly-supervised manners, we also compared class activation maps for each instance with manual masks of breast mass in terms of the Dice similarity coefficient and localization recall. The experimental results also demonstrate that the deep network with the adjustable convolution layers can clinically relevant features of breast mass and its surrounding area for both benign and malignant cases.

Proceedings Article | 13 March 2009 Paper

Automated 3D heart segmentation by search rays for building individual conductor models

Jaeil Kim, Seokyeol Kim, Kiwoong Kim, Jinah Park

Proceedings Volume 7261, 72611W (2009) https://doi.org/10.1117/12.813880

KEYWORDS: Heart, 3D modeling, Data modeling, Image segmentation, Target detection, Spherical lenses, 3D image processing, Detection and tracking algorithms, Visual process modeling, Magnetism

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Magnetocardiograph (MCG) is one of the most useful diagnosing tools for myocardial ischemic diseases and the conduction abnormality, since the technique directly measures magnetic fields generated by myocardial currents without distortion in a non-invasive way. To localize the current source accurately, building a patient-specific conductor model is indispensable. In this paper, we present the method to automatically construct a patient-specific three-dimensional (3D) mesh model of a human thorax and a heart consisting of pericardium and four chambers. We represent the standard thorax model by simplex meshes, and deform them to fit into the individual CT data to reconstruct accurate surface representations for the MCG conductor model. The deformable simplex mesh model deforms based on the external forces exerted by the edge and gradient components of the source volume data while its internal force acts to maintain the integrity of the shape. However, image driven deformation is often very sensitive to its initial position. Therefore, we suggest our solution to automatic region-of-interest (ROI) detection using search rays, which are casted to 3D volume images to identify the region of a heart based on both the radiodensity values and their continuity along the path of the rays. Upon automatic ROI detection with search rays, the initial position and orientation of the standard mesh model is determined, and each vertex of the model is respectively moved by the weighted sum of the internal and external forces to conform to the each patient's own thorax and heart shape while minimizing the user's input.

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