{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-interpretable-deep-hierarchical-semantic","title":"An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification","arxiv_id":"1806.00712","date":"2018-06-02","proceeding":null,"authors":["Shiwen Shen","Simon X. Han","Denise R. Aberle","Alex A. T. Bui","Willliam Hsu"],"abstract":"While deep learning methods are increasingly being applied to tasks such as\ncomputer-aided diagnosis, these models are difficult to interpret, do not\nincorporate prior domain knowledge, and are often considered as a \"black-box.\"\nThe lack of model interpretability hinders them from being fully understood by\ntarget users such as radiologists. In this paper, we present a novel\ninterpretable deep hierarchical semantic convolutional neural network (HSCNN)\nto predict whether a given pulmonary nodule observed on a computed tomography\n(CT) scan is malignant. Our network provides two levels of output: 1) low-level\nradiologist semantic features, and 2) a high-level malignancy prediction score.\nThe low-level semantic outputs quantify the diagnostic features used by\nradiologists and serve to explain how the model interprets the images in an\nexpert-driven manner. The information from these low-level tasks, along with\nthe representations learned by the convolutional layers, are then combined and\nused to infer the high-level task of predicting nodule malignancy. This unified\narchitecture is trained by optimizing a global loss function including both\nlow- and high-level tasks, thereby learning all the parameters within a joint\nframework. Our experimental results using the Lung Image Database Consortium\n(LIDC) show that the proposed method not only produces interpretable lung\ncancer predictions but also achieves significantly better results compared to\ncommon 3D CNN approaches.","url_abs":"http://arxiv.org/abs/1806.00712v1","url_pdf":"http://arxiv.org/pdf/1806.00712v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-interpretable-deep-hierarchical-semantic","repo_url":"https://github.com/qiuliwang/lidc-idri-toolbox-python","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}