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Yun Peng

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7 papers

1 author row

YNICL Journal 2023 Journal Article

A presurgical voxel-wise predictive model for cerebellar mutism syndrome in children with posterior fossa tumors

Wei Yang
Yiming Li
Zesheng Ying
Yingjie Cai
Xiaojiao Peng
HaiLang Sun
Jiashu Chen
Kaiyi Zhu

BACKGROUND: This study aimed to investigate cerebellar mutism syndrome (CMS)-related voxels and build a voxel-wise predictive model for CMS. METHODS: From July 2013 to January 2022, 188 pediatric patients diagnosed with posterior fossa tumor were included in this study, including 38 from a prospective cohort recruited between 2020 and January 2022, and the remaining from a retrospective cohort recruited in July 2013-Aug 2020. The retrospective cohort was divided into the training and validation sets; the prospective cohort served as a prospective validation set. Voxel-based lesion symptoms were assessed to identify voxels related to CMS, and a predictive model was constructed and tested in the validation and prospective validation sets. RESULTS: No significant differences were detected among these three data sets in CMS rate, gender, age, tumor size, tumor consistency, presence of hydrocephalus and paraventricular edema. Voxels related to CMS were mainly located in bilateral superior and inferior cerebellar peduncles and the superior part of the cerebellum. The areas under the curves for the model in the training, validation and prospective validation sets were 0.889, 0.784 and 0.791, respectively. CONCLUSIONS: Superior and inferior cerebellar peduncles and the superior part of the cerebellum were related to CMS, especially the right side, and voxel-based lesion-symptom analysis could provide valuable predictive information before surgery.

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IJCAI Conference 2021 Conference Paper

Graph Edit Distance Learning via Modeling Optimum Matchings with Constraints

Yun Peng
Byron Choi
Jianliang Xu

Graph edit distance (GED) is a fundamental measure for graph similarity analysis in many real applications. GED computation has known to be NP-hard and many heuristic methods are proposed. GED has two inherent characteristics: multiple optimum node matchings and one-to-one node matching constraints. However, these two characteristics have not been well considered in the existing learning-based methods, which leads to suboptimal models. In this paper, we propose a novel GED-specific loss function that simultaneously encodes the two characteristics. First, we propose an optimal partial node matching-based regularizer to encode multiple optimum node matchings. Second, we propose a plane intersection-based regularizer to impose the one-to-one constraints for the encoded node matchings. We use the graph neural network on the association graph of the two input graphs to learn the cross-graph representation. Our experiments show that our method is 4. 2x-103. 8x more accurate than the state-of-the-art methods on real-world benchmark graphs.

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YNIMG Journal 2019 Journal Article

Age-specific gray and white matter DTI atlas for human brain at 33, 36 and 39 postmenstrual weeks

Lei Feng
Hang Li
Kenichi Oishi
Virendra Mishra
Limei Song
Qinmu Peng
Minhui Ouyang
Jiaojian Wang

During the 3rd trimester, dramatic structural changes take place in the human brain, underlying the neural circuit formation. The survival rate of premature infants has increased significantly in recent years. The large morphological differences of the preterm brain at 33 or 36 postmenstrual weeks (PMW) from the brain at 40PMW (full term) make it necessary to establish age-specific atlases for preterm brains. In this study, with high quality (1. 5 × 1. 5 × 1. 6 mm3 imaging resolution) diffusion tensor imaging (DTI) data obtained from 84 healthy preterm and term-born neonates, we established age-specific preterm and term-born brain templates and atlases at 33, 36 and 39PMW. Age-specific DTI templates include a single-subject template, a population-averaged template with linear transformation and a population-averaged template with nonlinear transformation. Each of the age-specific DTI atlases includes comprehensive labeling of 126 major gray matter (GM) and white matter (WM) structures, specifically 52 cerebral cortical structures, 40 cerebral WM structures, 22 brainstem and cerebellar structures and 12 subcortical GM structures. From 33 to 39 PMW, dramatic morphological changes of delineated individual neural structures such as ganglionic eminence and uncinate fasciculus were revealed. The evaluation based on measurements of Dice ratio and L1 error suggested reliable and reproducible automated labels from the age-matched atlases compared to labels from manual delineation. Applying these atlases to automatically and effectively delineate microstructural changes of major WM tracts during the 3rd trimester was demonstrated. The established age-specific DTI templates and atlases of 33, 36 and 39 PMW brains may be used for not only understanding normal functional and structural maturational processes but also detecting biomarkers of neural disorders in the preterm brains.

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IS Journal 2002 Journal Article

ITtalks: a case study in the Semantic Web and DAML+OIL

R.S. Cost
T. Finin
A. Joshi
Yun Peng
C. Nicholas
I. Soboroff
H. Chen
L. Kagal

Semantic Web markup languages will improve the automated gathering and processing of information and help integrate multiagent systems with the existing information infrastructure. The authors, describe their ITtalks system and discuss how Semantic Web concepts and DAML+OIL extend its ability to provide an intelligent online service.

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AAAI Conference 1999 Short Paper

Learning in Broker Agent

Xiaocheng Luan
Yun Peng
Timothy Finin
University of Maryland Baltimore County

One of the common ways to achieve interoperability among the autonomous agents is to use a broker agent (or a facilitator). Simple broker agents provide match-making services based on the capability information volunteered by individual agents and the (recommendation) request. The problem is, even with a very good agent capability description language and a powerful match-making mechanism, if the actual capability information volunteered by each individual agent is not accurate, it won’t be of much help. Given that the autonomous agents might be written by different people, at different time, and for different purpose, this is likely to occur. This work is an attempt to solve such problems by incorporating learning into broker agents so that the broker agents can capture more accurate information about the capabilities of individual agents.

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AIIM Journal 1994 Journal Article

High-specificity neurological localization using a connectionist model

Stanley Tuhrim
James A. Reggia
Yun Peng

Most previous connectionist models for diagnosis have been developed using error backpropagation. While these systems function reasonably well, they have been limited by their need for a large database of test cases, to situations where a single disorder is present, and by the large number of connections required between fully-connected sets of processing units. Here we describe a recently developed connectionist model that overcomes these limitations. This approach can reuse existing causal knowledge bases, works well in situations where multiple disorders can occur simultaneously, and does not require fully-connected sets of processing units. We demonstrate that the accuracy of this model is comparable to that of more conventional AI programs using the same knowledge base in determining precisely the site of brain damage in a group of 50 stroke patients. These results support the conclusion that connectionist models can effectively use pre-existing causal knowledge bases from AI systems, and that they can function accurately when handling actual clinical problems.

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AAAI Conference 1986 Conference Paper

Plausibility of Diagnostic Hypotheses: The Nature of Simplicity

Yun Peng

In general diagnostic problems multiple disorders can occur simultaneously. AI systems have traditionally handled the potential combinatorial explosion of possible hypotheses in such problems by focusing attention on a few "most plausible" ones. This raises the issue of establishing what makes one hypothesis more plausible than others. Typically a hypothesis (a set of disorders) must not only account for the given manifestations, but it must also satisfy some notion of simplicity (or coherency, or parsimony, etc) to be considered. While various criteria for simplicity have been proposed in the past, these have been based on intuitive and subjective grounds. In this paper, we address the issue of if and when several previously-proposed criteria of parsimony are reasonable in the sense that they are guaranteed to at least identify the most probable hypothesis. Hypothesis likelihood is calculated using a recent extension of Bayesian classification theory for multimembership classification in causal diagnostic domains. The significance of this result is that it is now possible to decide objectively a priori the appropriateness of different criteria for simplicity in developing an inference method for certain classes of general diagnostic problems.

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