Yong Man Ro
Korea Advanced Institute of Science and Technology · 情報科学
研究室紹介
Professor Yong Man Ro's research lab specializes in medical image analysis and computer-aided diagnosis (CAD), with a strong focus on improving diagnostic accuracy and interpretability in breast cancer detection. The lab develops advanced image processing and machine learning techniques—particularly sparse representation, deep learning, and texture feature extraction—for analyzing mammograms, digital breast tomosynthesis (DBT), and transvaginal ultrasound images. Key research directions include robust segmentation of anatomical structures like the endometrium, reducing false positives in mass detection, and designing visually interpretable deep learning models aligned with clinical reporting standards such as BIRADS.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15MPEG7 standardization work has started with the aims of providing fundamental tools for describing multimedia contents. MPEG7 defines the syntax and semantics of de-scriptors and description schemes so that they may he used as fundamental tools for multimedia content description. In this paper, we introduce a texture based image descrip-tion and retrieval method, which is adopted as the homo-geneous texture descriptor in the visual part of the MPEG 7 final committee draft. The current MPEG7 homo
BACKGROUND: Breast cancer is the leading cause of both incidence and mortality in women population. For this reason, much research effort has been devoted to develop Computer-Aided Detection (CAD) systems for early detection of the breast cancers on mammograms. In this paper, we propose a new and novel dictionary configuration underpinning sparse representation based classification (SRC). The key idea of the proposed algorithm is to improve the sparsity in terms of mass margins for the purpose o
PURPOSE: Transvaginal ultrasound imaging provides useful information for diagnosing endometrial pathologies and reproductive health. Endometrium segmentation in transvaginal ultrasound (TVUS) images is very challenging due to ambiguous boundaries and heterogeneous textures. In this study, we developed a new segmentation framework which provides robust segmentation against ambiguous boundaries and heterogeneous textures of TVUS images. METHODS: To achieve endometrium segmentation from TVUS images
In this paper, a new and novel approach is designed for extracting local binary pattern (LBP) texture features from the computer-identified mass regions, aiming to reduce false-positive (FP) detection in a computerized mass detection framework. The proposed texture feature, the so-called multiresolution LBP feature, is well able to characterize the regional texture patterns of core and margin regions of a mass, as well as to preserve the spatial structure information of the mass. In addition, to
Recently, deep learning technology has achieved various successes in medical image analysis studies including computer-aided diagnosis (CADx). However, current CADx approaches based on deep learning have a limitation in interpreting diagnostic decisions. The limited interpretability is a major challenge for practical use of current deep learning approaches. In this paper, a novel visually interpretable deep network framework is proposed to provide diagnostic decisions with visual interpretation.
Characterization of masses in computer-aided detection systems for digital breast tomosynthesis (DBT) is an important step to reduce false positive (FP) rates. To effectively differentiate masses from FPs in DBT, discriminative mass feature representation is required. In this paper, we propose a new latent feature representation boosted by depth directional long-term recurrent learning for characterizing malignant masses. The proposed network is designed to encode mass characteristics in two par