Discriminative Pathological Context Detection in Thoracic Images Based on Multi-level Inference
作者： Yang Song , Weidong Cai , Stefan Eberl , Michael J. Fulham , Dagan Feng
DOI: 10.1007/978-3-642-23626-6_24
关键词: Radiography 、 Mediastinum 、 Positron emission tomography 、 Lung cancer 、 Context (language use) 、 Artificial intelligence 、 Tomography 、 Discriminative model 、 Computer vision 、 Inference 、 Medicine
摘要: Positron emission tomography - computed (PET-CT) is now accepted as the best imaging technique to accurately stage lung cancer. The consistent and accurate interpretation of PETCT images, however, not a trivial task. We propose discriminative, multi-level learning inference method automatically detect pathological contexts in thoracic PET-CT i.e. primary tumor its spatial relationships within mediastinum, disease regional lymph nodes. detection results can also be used features retrieve similar images with previous diagnosis from an database reference set aid physicians scan interpretation. Our evaluation clinical data cancer patients suggests our approach highly accurate.
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