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Title: New approaches to automated annotation of pathology-level findings in medical images
Keywords: medical image classification; machine learning; medical informatics; image annotation
Issue Date: 3-Jul-2013
Citation: DINH THIEN ANH (2013-07-03). New approaches to automated annotation of pathology-level findings in medical images. ScholarBank@NUS Repository.
Abstract: Medical image annotation aims to improve the effectiveness and efficiency of keyword-based image retrieval. In this work, we focus on automated pathology annotation that tries to identify potential pathologies, abnormalities and diseases from brain images. This is a challenging task because pathology annotation demands a deep understanding of the structural and functional changes induced by diseases. Existing works in pathological annotation often require large and fully annotated training data, reliable segmentation, and domain knowledge for hand-crafted feature extraction and selection. Since these prerequisites are not always feasible, they reduce the level of automation, desirability, and practicality of the annotation systems. To mitigate the requirements of annotated training data and reliable segmentation, we propose to use probabilistic generative models, since they support the integration of expert knowledge and effectively handle the uncertainties inherent in the images and segmentation. However, when a priori knowledge is not available, these generative models are not able to achieve their best performance. In this case, we suggest using a discriminative model which incorporates an automated feature selection method to tackle the problem. Specifically, sparse group lasso provides a flexible selection mechanism that helps to handle annotation problems without relying on the domain knowledge. The performance of existing annotation methods heavily depends on the quality of hand-crafted features extracted from an automatic image segmentation. To achieve good performance, constructing the system requires a considerable amount of manual work. We propose to combine an unsupervised feature extraction technique with a case-based classification in an ensemble learning framework to improve the adaptability and automation of the annotation systems. The unsupervised nature of this non-parametric technique can significantly reduce the time and effort for system calibration. To evaluate these approaches, we select two important neurological disorders - ischemic stroke and traumatic brain injury, as illustrative domains because imaging findings of these diseases play significant roles in their diagnosis. Despite the additional challenges due to the relaxation of the common prerequisites in existing systems, our proposed frameworks still show reasonable performance. An informal evaluation with expert users has also demonstrated the practical promise of the proposed system.
Appears in Collections:Ph.D Theses (Open)

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