Lecture Notes in Computer Science
DOI: 10.1007/978-3-642-23944-1_12 Conference: Prostate Cancer Imaging. Image Analysis and Image-Guided Interventions - International Workshop, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 22, 2011. Proceedings
Prostate cancer is one of the most frequently diagnosed neoplasy and its presence can only be confirmed by biopsy. Due to the high number of false positives, Computer Aided Detection (CAD) systems can be used to reduce the number of cores requested for an accurate diagnosis. This work proposes a CAD procedure for cancer detection in Ultrasound images based on a learning scheme which exploits a novel semi-supervised learning (SSL) algorithm for reducing data collection effort and avoiding collected data wasting. The ground truth database comprises the RF-signals acquired during biopsies and the corresponding tissue samples histopathological outcome. A comparison to a state-of-art CAD scheme based on supervised learning demonstrates the effectiveness of the proposed SSL procedure at enhancing CAD performance. Experiments on ground truth images from biopsy findings show that the proposed CAD scheme is effective at improving the efficiency of the biopsy protocol.
Available from: Jan-Shin Ho
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ABSTRACT: Efficient random access is one of the key designs in code-division multiple-access (CDMA) cellular systems. Random access is used by user equipment for initial access, requesting dedicated channels and transmitting short packet data to a base station. In random access, a preamble part is usually devised for fast and reliable burst synchronization, which is essential in order to avoid excessive access delay and/or repeated transmissions that may reduce the overall system capacity. This paper investigates the issue of burst synchronization for the slotted random access with preamble power ramping in the reverse link of CDMA systems. A flexible burst synchronizer based on a parallel-serial code-phase detector is proposed, which can be easily configured to achieve different complexity/performance tradeoffs. A general analysis is also presented with important design parameters being taken into account, including the number of correlators, power control error, power ramping step, diversity order, frequency offset, multipath combining, and others. The analysis is verified by computer simulations.
Available from: Mahdi Tabassian
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ABSTRACT: In this paper a real-time computer-aided biopsy (rtCAB) system is presented to support prostate cancer diagnosis. Different types of features are extracted from trans-rectal ultrasound data and an ensemble learning algorithm is used in classification phase. A new label assignment method is also employed to provide soft or crisp class labels for uncertain data. The proposed model could be implemented in parallel on GPU using CUDA platform to provide real-time support to physician during biopsy. Experiments on ground truth images from biopsy finding demonstrate that the proposed approach can properly deal with uncertain data and is able to provide better results than some examined supervised and semi-supervised classifiers.
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