ArticleIEEE transactions on medical imaging2014
Predicting visual semantic descriptive terms from radiological image data: preliminary results with liver lesions in CT.
Article in IEEE transactions on medical imaging, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ontologies for Liver Diseases Representation: A Systematic Literature Review.Journal of digital imaging · 2020Pooled it
- Characterization of spatially mapped volumetric molecular ultrasound signals for predicting response to anti-vascular therapy.Scientific reports · 2023Article
- Radiomics in immuno-oncology.Immuno-oncology technology · 2021Review
- Spatial Characterization of Tumor Perfusion Properties from 3D DCE-US Perfusion Maps are Early Predictors of Cancer Treatment Response.Scientific reports · 2020Article
- Generate Structured Radiology Report from CT Images Using Image Annotation Techniques: Preliminary Results with Liver CT.Journal of digital imaging · 2020Article
- Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies.Journal of digital imaging · 2020Article
- The use of texture-based radiomics CT analysis to predict outcomes in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy.The British journal of radiology · 2019Article
- Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.Cancer · 2018Review
- DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.International journal of computer assisted radiology and surgery · 2018Article
- Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Computerized Prediction of Radiological Observations Based on Quantitative Feature Analysis: Initial Experience in Liver Lesions.Journal of digital imaging · 2017Article
- Method for Adapting the Grayscale Standard Display Function to the Aging Eye.Journal of digital imaging · 2017Article
- Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging.IEEE transactions on bio-medical engineering · 2016Article
- Dictionary Pruning with Visual Word Significance for Medical Image Retrieval.Neurocomputing · 2016Article
- On combining image-based and ontological semantic dissimilarities for medical image retrieval applications.Medical image analysis · 2014Article
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5 authors.
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Abstract
We describe a framework to model visual semantics of liver lesions in CT images in order to predict the visual semantic terms (VST) reported by radiologists in describing these lesions. Computational models of VST are learned from image data using linear combinations of high-order steerable Riesz wavelets and support vector machines (SVM). In a first step, these models are used to predict the presence of each semantic term that describes liver lesions. In a second step, the distances between all VST models are calculated to establish a nonhierarchical computationally-derived ontology of VST containing inter-term synonymy and complementarity. A preliminary evaluation of the proposed framework was carried out using 74 liver lesions annotated with a set of 18 VSTs from the RadLex ontology. A leave-one-patient-out cross-validation resulted in an average area under the ROC curve of 0.853 for predicting the presence of each VST. The proposed framework is expected to foster human-computer synergies for the interpretation of radiological images while using rotation-covariant computational models of VSTs to 1) quantify their local likelihood and 2) explicitly link them with pixel-based image content in the context of a given imaging domain.
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