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Articles 151 - 161 of 161
Full-Text Articles in Physical Sciences and Mathematics
Set-Based Cascading Approaches For Magnetic Resonance (Mr) Image Segmentation (Scamis), Jiang Liu, Tze-Yun Leong, Kin Ban Chee, Boon Pin Tan, Borys Shuter, Shih Chang Wang
Set-Based Cascading Approaches For Magnetic Resonance (Mr) Image Segmentation (Scamis), Jiang Liu, Tze-Yun Leong, Kin Ban Chee, Boon Pin Tan, Borys Shuter, Shih Chang Wang
Research Collection School Of Computing and Information Systems
This paper introduces Set-based Cascading Approach for Medical Image Segmentation (SCAMIS), a new methodology for segmentation of medical imaging by integrating a number of algorithms. Existing approaches typically adopt the pipeline methodology. Although these methods provide promising results, the results generated are still susceptible to over-segmentation and leaking. In our methodology, we describe how set operations can be utilized to better overcome these problems. To evaluate the effectiveness of this approach, Magnetic Resonance Images taken from a teaching hospital research programme have been utilised, to reflect the real world quality needed for testing in patient datasets. A comparison between the …
Batch Mode Active Learning And Its Applications To Medical Image Classification, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Batch Mode Active Learning And Its Applications To Medical Image Classification, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Research Collection School Of Computing and Information Systems
The goal of active learning is to select the most informative examples for manual labeling. Most of the previous studies in active learning have focused on selecting a single unlabeled example in each iteration. This could be inefficient since the classification model has to be retrained for every labeled example. In this paper, we present a framework for "batch mode active learning" that applies the Fisher information matrix to select a number of informative examples simultaneously. The key computational challenge is how to efficiently identify the subset of unlabeled examples that can result in the largest reduction in the Fisher …
Fortifying Password Authentication In Integrated Healthcare Delivery Systems, Yanjiang Yang, Robert H. Deng, Feng Bao
Fortifying Password Authentication In Integrated Healthcare Delivery Systems, Yanjiang Yang, Robert H. Deng, Feng Bao
Research Collection School Of Computing and Information Systems
Integrated Delivery Systems (IDSs) now become a primary means of care provision in healthcare domain. However, existing password systems (under either the single-server model or the multi-server model) do not provide adequate security when applied to IDSs. We are thus motivated to present a practical password authentication system built upon a novel two-server model. We generalize the two-server model to an architecture of a single control server supporting multiple service servers, tailored to the organizational structure of IDSs. The underlying user authentication and key exchange protocols we propose are password-only, neat, efficient, and robust against off-line dictionary attacks mounted by …
Method For Identifying Individuals, Manoj Thulasidas
Method For Identifying Individuals, Manoj Thulasidas
Research Collection School Of Computing and Information Systems
A method and system for identifying a subject comprises obtaining a digitised recording of an electrocardiogram measurement of the subject to be identified, the digitised recording being a cyclic waveform having a peak amplitude. The digitised recording is normalised to reduce variations due to physiological effects, and the normalised recording is processed to determine a feature vector in the frequency domain. The distance between the determined feature vector and a predetermined feature vector is measured to identify the subject.
Mining Of Correlated Rules In Genome Sequences, L. Lin, L. Wong, Tze-Yun Leong, P. S. Lai
Mining Of Correlated Rules In Genome Sequences, L. Lin, L. Wong, Tze-Yun Leong, P. S. Lai
Research Collection School Of Computing and Information Systems
With the huge amount of data collected by scientists in the molecular genetics community in recent years, there exists a need to develop some novel algorithms based on existing data mining techniques to discover useful information from genome databases. We propose an algorithm that integrates the statistical method, association rule mining, and classification rule mining in the discovery of allelic combinations of genes that are peculiar to certain phenotypes of diseased patients.
Nonparametric Techniques To Extract Fuzzy Rules For Breast Cancer Diagnosis Problem, Manish Sarkar, Tze-Yun Leong
Nonparametric Techniques To Extract Fuzzy Rules For Breast Cancer Diagnosis Problem, Manish Sarkar, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
This paper addresses breast cancer diagnosis problem as a pattern classification problem. Specifically, the problem is studied using Wisconsin-Madison breast cancer data set. Fuzzy rules are generated from the input-output relationship so that the diagnosis becomes easier and transparent for both patients and physicians. For each class, at least one training pattern is chosen as the prototype, provided (a) the maximum membership of the training pattern is in the given class, and (b) among all the training patterns, the neighborhood of this training pattern has the least fuzzy-rough uncertainty in the given class. Using the fuzzy-rough uncertainty, a cluster is …
Decision Support Methods In Diabetic Patient Management By Insulin Administration Neural Network Vs. Induction Methods For Knowledge Classification, B. V. Ambrosiadou, S. Vadera, Venky Shankaraman, D. Goulis, G. Gogou
Decision Support Methods In Diabetic Patient Management By Insulin Administration Neural Network Vs. Induction Methods For Knowledge Classification, B. V. Ambrosiadou, S. Vadera, Venky Shankaraman, D. Goulis, G. Gogou
Research Collection School Of Computing and Information Systems
Diabetes mellitus is now recognised as a major worldwide public health problem. At present, about 100 million people are registered as diabetic patients. Many clinical, social and economic problems occur as a consequence of insulin-dependent diabetes. Treatment attempts to prevent or delay complications by applying ‘optimal’ glycaemic control. Therefore, there is a continuous need for effective monitoring of the patient. Given the popularity of decision tree learning algorithms as well as neural networks for knowledge classification which is further used for decision support, this paper examines their relative merits by applying one algorithm from each family on a medical problem; …
Decision Analytic Approach To Severe Head Injury Management., Harmanec D., Tze-Yun Leong, Sundaresh S., Poh K., Yeo T., Ng I., Lew T.
Decision Analytic Approach To Severe Head Injury Management., Harmanec D., Tze-Yun Leong, Sundaresh S., Poh K., Yeo T., Ng I., Lew T.
Research Collection School Of Computing and Information Systems
Severe head injury management in the intensive care unit is extremely challenging due to the complex domain, the uncertain intervention efficacies, and the time-critical setting. We adopt a decision analytic approach to automate the management process. We document our experience in building a simplified influence diagram that involves about 3000 numerical parameters. We identify the inherent problems in structuring a model with unclear domain relationships, numerous interacting variables, and real-time multiple inputs. We analyze the effectiveness and limitations of the decision analytic approach and present a set of desiderata for effective knowledge acquisition in this setting. We also propose a …
Modelling Medical Decisions In Dynamol: A New General Framework Of Dynamic Decision Analysis, Tze-Yun Leong, Cungen Cao
Modelling Medical Decisions In Dynamol: A New General Framework Of Dynamic Decision Analysis, Tze-Yun Leong, Cungen Cao
Research Collection School Of Computing and Information Systems
Dynamic decision analysis concerns decision problems in which both time and uncertainty are explicitly considered. We present a new dynamic decision analysis framework, called DynamoL, that supports graphical presentation of the decision factors in multiple perspectives. To alleviate the difficulty in assessing conditional probabilities over time in dynamic decision models, DynaMoL incorporates a Bayesian learning system to automatically learn the probabilistic parameters from large medical databases. We describe the DynaMoL modeling and learning architecture through a medical decision problem on the optimal follow-up schedule for patients after curative colorectal cancer surgery. We also show that the modeling experience and results …
Dynamic Decision Modeling In Medicine: A Critique Of Existing Formalisms, Tze-Yun Leong
Dynamic Decision Modeling In Medicine: A Critique Of Existing Formalisms, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
Dynamic decision models are frameworks for modeling and solving decision problems that take into explicit account the effects of time. These formalisms are based on structural and semantical extensions of conventional decision models, e.g., decision trees and influence diagrams, with the mathematical definitions of finite-state semi-Markov processes. This paper identifies the common theoretical basis of existing dynamic decision modeling formalisms, and compares and contrasts their applicability and efficiency. It also argues that a subclass of such dynamic decision problems can be formulated and solved more effectively with non-graphical techniques. Some insights gained from this exercise on automating the dynamic decision …
Representation Requirements For Supporting Knowledge-Based Construction Of Decision-Models In Medicine, Tze-Yun Leong
Representation Requirements For Supporting Knowledge-Based Construction Of Decision-Models In Medicine, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
This paper analyzes the medical knowledge required for formulating decision models in the domain of pulmonary infectious diseases (PIDs) with acquired immunodeficiency syndrome (AIDS). Aiming to support dynamic decision-modeling, the knowledge characterization focuses on the ontology of the clinical decision problem. Relevant inference patterns and knowledge types are identified.