Semantic Annotation and Classification of Mammography Images using Ontologies

Pereira, Juliana Wolf; Ribeiro, Marcela Xavier

doi:10.1109/cbms52027.2021.00043

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…For instance, in the work [de Sousa Fogaça and Bueno 2020], the authors mapped color-based features into the multidimensional space to estimate the trajectory of objects by simulating their evolution over time. In [Pereira and Ribeiro 2021], the authors explored visual features extracted from mammograms for the semantic annotation and classification of images using an ontology. Low-level features have been widely applied to validate the indexing capabilities of Metric Access Methods Moriyama et al 2021].…”

Section: Introductionmentioning

confidence: 99%

FeatSet+: Visual Features Extracted from Public Image Datasets

Cazzolato

Scabora

Zabot

et al. 2022

jidm

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Real-world applications generate large amounts of images every day. With the generalized use of social media, users frequently share images acquired by smartphones. Also, hospitals, clinics, exhibits, factories, and other facilities generate images with potential use for many applications. Processing the generated images usually requires feature extraction, which can be time-consuming and laborious. In this paper, we present FeatSet+, a compilation of color, texture and shape visual features extracted from 17 open image datasets reported in the literature. FeatSet+ provides a collection of 11 distinct visual features, extracted by well-known Feature Extraction Methods (FEMs) such as LBP, Haralick, and Color Layout. We organized the available features in a standard collection, including the metadata and labels, when available. Eleven of the datasets also contain classes, which aid the evaluation of supervised methods such as classifiers and clustering tasks. FeatSet+ is available for download in a public repository as sql scripts and csv files. Additionally, FeatSet+ provides a description of the domain of each dataset, including the reference to the original work and link. We show the potential applicability of FeatSet+ in four computational tasks: multi-attribute analysis and retrieval, visual analysis using Multidimensional Scaling (MDS) and Principal Components Analysis (PCA), global feature classification, and dimensionality reduction. FeatSet+ can be employed to evaluate supervised and non-supervised learning tasks, also widely supporting Content-Based Image Retrieval (CBIR) applications and complex data indexing using Metric Access Methods (MAMs).

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Section: Introductionmentioning

confidence: 99%

FeatSet+: Visual Features Extracted from Public Image Datasets

Cazzolato

Scabora

Zabot

et al. 2022

jidm

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show abstract

Bioimage Databases

Abu,

Jegathisan,

Dhillon

2024

Reference Module in Life Sciences

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Construction of a knowledge graph for breast cancer diagnosis based on Chinese electronic medical records: development and usability study

Li,

Sun,

Tang

et al. 2023

BMC Med Inform Decis Mak

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Background Electronic medical records (EMRs) contain a wealth of information related to breast cancer diagnosis and treatment. Extracting relevant features from these medical records and constructing a knowledge graph can significantly contribute to an efficient data analysis and decision support system for breast cancer diagnosis. Methods An approach was proposed to develop a workflow for effectively extracting breast cancer-related features from Chinese breast cancer mammography reports and constructing a knowledge graph for breast cancer diagnosis. Firstly, the concept layer of the knowledge graph for breast cancer diagnosis was constructed based on breast cancer diagnosis and treatment guidelines, along with insights from clinical experts. .Next, a BiLSTM-Highway-CRF model was designed to extract the mammography features, which formed the data layer of the knowledge graph. Finally, the knowledge graph was constructed by combining the concept layer and the data layer in a Neo4j graph data platform, and then applied in visualization analysis, semantic query and computer assisted diagnosis. Results Mammographic features were extracted from a total of 1171 mammography examination reports. The overall extraction performance of the model achieved an accuracy rate of 97.16%, a recall rate of 98.06%, and a F1 score of 97.61%. Additionally, 47,660 relationships between entities were identified based on the four different types of relationships defined in the concept layer. The knowledge graph for breast cancer diagnosis was constructed after inputting mammographic features and relationships into the Neo4j graph data platform. The model was assessed from the concept layer, data layer, and application layer perspectives, and showed promising results. Conclusions The proposed workflow is applicable for constructing knowledge graphs for breast cancer diagnosis based on Chinese EMRs. This study serves as a reference for the rapid design, construction, and application of knowledge graphs for diagnosis and treatment of other diseases. Furthermore, it offers a potential solution to address the issues of limited data sharing and format inconsistencies present in Chinese EMR data.

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Semantic Annotation and Classification of Mammography Images using Ontologies

Cited by 4 publications

References 15 publications

FeatSet+: Visual Features Extracted from Public Image Datasets

FeatSet+: Visual Features Extracted from Public Image Datasets

Bioimage Databases

Construction of a knowledge graph for breast cancer diagnosis based on Chinese electronic medical records: development and usability study

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