{"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/evidential-concept-embedding-models-towards","title":"Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis","arxiv_id":"2406.19130","date":"2024-06-27","proceeding":null,"authors":["Yibo Gao","Zheyao Gao","Xin Gao","Yuanye Liu","Bomin Wang","Xiahai Zhuang"],"abstract":"Due to the high stakes in medical decision-making, there is a compelling demand for interpretable deep learning methods in medical image analysis. Concept Bottleneck Models (CBM) have emerged as an active interpretable framework incorporating human-interpretable concepts into decision-making. However, their concept predictions may lack reliability when applied to clinical diagnosis, impeding concept explanations' quality. To address this, we propose an evidential Concept Embedding Model (evi-CEM), which employs evidential learning to model the concept uncertainty. Additionally, we offer to leverage the concept uncertainty to rectify concept misalignments that arise when training CBMs using vision-language models without complete concept supervision. With the proposed methods, we can enhance concept explanations' reliability for both supervised and label-efficient settings. Furthermore, we introduce concept uncertainty for effective test-time intervention. Our evaluation demonstrates that evi-CEM achieves superior performance in terms of concept prediction, and the proposed concept rectification effectively mitigates concept misalignments for label-efficient training. Our code is available at https://github.com/obiyoag/evi-CEM.","url_abs":"https://arxiv.org/abs/2406.19130v1","url_pdf":"https://arxiv.org/pdf/2406.19130v1.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":"evidential-concept-embedding-models-towards","repo_url":"https://github.com/obiyoag/evi-cem","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2406.19130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.19130"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/obiyoag/evi-cem","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran":6},"by_repo_kind":{"official":{"samples":6,"ran":6,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"f6e2a42cc26a7564","entry":"compute_bin_accuracy","repo":"obiyoag/evi-cem","repo_kind":"official","path":"utils.py","file_url":"https://github.com/obiyoag/evi-cem/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"f6e2a42cc26a7564"}},{"code_sha256_prefix":"ef8dfce510a10995","entry":"construct_callbacks","repo":"obiyoag/evi-cem","repo_kind":"official","path":"utils.py","file_url":"https://github.com/obiyoag/evi-cem/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"ef8dfce510a10995"}},{"code_sha256_prefix":"287392eae1fafe8c","entry":"get_cav","repo":"obiyoag/evi-cem","repo_kind":"official","path":"learn_cavs.py","file_url":"https://github.com/obiyoag/evi-cem/blob/HEAD/learn_cavs.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"287392eae1fafe8c"}},{"code_sha256_prefix":"6cd52c2775a843cb","entry":"get_embeddings","repo":"obiyoag/evi-cem","repo_kind":"official","path":"learn_cavs.py","file_url":"https://github.com/obiyoag/evi-cem/blob/HEAD/learn_cavs.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"6cd52c2775a843cb"}},{"code_sha256_prefix":"ec1838ce50f98f86","entry":"init_logger","repo":"obiyoag/evi-cem","repo_kind":"official","path":"utils.py","file_url":"https://github.com/obiyoag/evi-cem/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"ec1838ce50f98f86"}},{"code_sha256_prefix":"6a9176a05e17c1cc","entry":"learn_concept_bank","repo":"obiyoag/evi-cem","repo_kind":"official","path":"learn_cavs.py","file_url":"https://github.com/obiyoag/evi-cem/blob/HEAD/learn_cavs.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"6a9176a05e17c1cc"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}