Scalable Medical Image Understanding by Fusing Cross-Modal Object Recognition with Formal Domain Semantics

Möller, Manuel; Sintek, Michael; Buitelaar, Paul; Mukherjee, Saikat; Zhou, Xiang; Freund, Joerg

doi:10.1007/978-3-540-92219-3_29

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…(2) To generate the spatial relations among these landmarks in the format of the FMA we had to (1) (3) To obtain a set of generic spatial relation instances-our model-we compared the data of different volume data sets from different patients. Next, we systematically eliminated contradictory tuples.…”

Section: Approach and Applied Methodsmentioning

confidence: 99%

Extending the Foundational Model of Anatomy with Automatically Acquired Spatial Relations

et al. 2009

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Section: Approach and Applied Methodsmentioning

confidence: 99%

Extending the Foundational Model of Anatomy with Automatically Acquired Spatial Relations

et al. 2009

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“…In the context of knowledge codification, formal knowledge refers to knowledge that is codified. Scientific knowledge can be intuitively defined as formal knowledge when adopted into the domain knowledge base (Hjørland & Albrechtsen, 1995;Möller, Sintek et al, 2008;Wang, Hamilton & Bither, 2005). Correspondingly, informal knowledge refers to the knowledge entities that were not codified or adopted into the knowledge base.…”

Section: Introductionmentioning

confidence: 99%

“…Formal knowledge is usually described by an ontological approach, which is the formal, explicit specification of a shared conceptualization of a domain. The machine interpretability of formal knowledge (codified knowledge) has been increasingly critical to improving the performance of information retrieval and image understanding (Möller, et al, 2008;Wildemuth, 2004) and organizing solutions to other real problems that exist in a specific research domain (Cardoso, Da Silveira, & Pruski, 2020;Tsatsaronis, Varlamis, 2013). Thus, understanding different transition patterns of formal knowledge is critical to tracking credible and valuable scientific knowledge and promoting innovation in science and technology.…”

Section: Introductionmentioning

confidence: 99%

Understanding knowledge role transitions: A perspective of knowledge codification

Yang

Yong

et al. 2022

Quantitative Science Studies

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Informal knowledge constantly transitions into formal domain knowledge in the dynamic knowledge base. This article focuses on an integrative understanding of the knowledge role transition from the perspective of knowledge codification. The transition process is characterized by several dynamics involving a variety of bibliometric entities, such as authors, keywords, institutions, and venues. We thereby designed a series of temporal and cumulative indicators to respectively explore transition possibility (whether new knowledge could be transitioned into formal knowledge) and transition pace (how long it would take). By analyzing the large-scale metadata of publications that contain informal knowledge and formal knowledge in the PubMed database, we find that multi-dimensional variables are essential to comprehensively understand knowledge role transition. More significantly, (a) the early funding support is more important for improving transition pace; (b) the journal impact has a positive correlation with the transition possibility but a negative correlation with transition pace; (c) the weaker knowledge relatedness raises the transition possibility, whereas the stronger knowledge relatedness improves the transition pace. Peer Review https://publons.com/publon/10.1162/qss_a_00221

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“…The research on semantic annotation and retrieval described in this paper is part of a broader effort to understand the semantic of medical images in the THESEUS MEDICO [12] project.…”

Section: Introductionmentioning

confidence: 99%

RadSem: Semantic Annotation and Retrieval for Medical Images

Möller

Regel

Sintek

2009

Lecture Notes in Computer Science

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We present a tool for semantic medical image annotation and retrieval. It leverages the MEDICO ontology which covers formal background information from various biomedical ontologies such as the Foundational Model of Anatomy (FMA), terminologies like ICD-10 and RadLex and covers various aspects of clinical procedures. This ontology is used during several steps of annotation and retrieval: (1) We developed an ontology-driven metadata extractor for the medical image format DI-COM. Its output contains, e. g., person name, age, image acquisition parameters, body region, etc. (2) The output from (1) is used to simplify the manual annotation by providing intuitive visualizations and to provide a preselected subset of annotation concepts. Furthermore, the extracted metadata is linked together with anatomical annotations and clinical findings to generate a unified view of a patient's medical history. (3) On the search side we perform query expansion based on the structure of the medical ontologies. (4) Our ontology for clinical data management allows us to link and combine patients, medical images and annotations together in a comprehensive result list. (5) The medical annotations are further extended by links to external sources like Wikipedia to provide additional information. 1 1 This research has been supported in part by the THESEUS Program in the MEDICO Project, which is funded by the German Federal Ministry of Economics and Technology under the grant number 01MQ07016. The responsibility for this publication lies with the authors.

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Scalable Medical Image Understanding by Fusing Cross-Modal Object Recognition with Formal Domain Semantics

Cited by 6 publications

References 19 publications

Extending the Foundational Model of Anatomy with Automatically Acquired Spatial Relations

Extending the Foundational Model of Anatomy with Automatically Acquired Spatial Relations

Understanding knowledge role transitions: A perspective of knowledge codification

RadSem: Semantic Annotation and Retrieval for Medical Images

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