{"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/regression-concept-vectors-for-bidirectional","title":"Regression Concept Vectors for Bidirectional Explanations in Histopathology","arxiv_id":"1904.04520","date":"2019-04-09","proceeding":null,"authors":["Mara Graziani","Vincent Andrearczyk","Henning Müller"],"abstract":"Explanations for deep neural network predictions in terms of domain-related\nconcepts can be valuable in medical applications, where justifications are\nimportant for confidence in the decision-making. In this work, we propose a\nmethodology to exploit continuous concept measures as Regression Concept\nVectors (RCVs) in the activation space of a layer. The directional derivative\nof the decision function along the RCVs represents the network sensitivity to\nincreasing values of a given concept measure. When applied to breast cancer\ngrading, nuclei texture emerges as a relevant concept in the detection of tumor\ntissue in breast lymph node samples. We evaluate score robustness and\nconsistency by statistical analysis.","url_abs":"http://arxiv.org/abs/1904.04520v1","url_pdf":"http://arxiv.org/pdf/1904.04520v1.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":"regression-concept-vectors-for-bidirectional","repo_url":"https://github.com/medgift/iMIMIC-RCVs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"breast-cancer-detection","task_name":"Breast Cancer Detection"},{"task_slug":"breast-cancer-histology-image-classification","task_name":"Breast Cancer Histology Image Classification"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"histopathological-image-classification","task_name":"Histopathological Image Classification"},{"task_slug":"sensitivity","task_name":"Sensitivity"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"dtw","method_name":"DTW"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.04520","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}