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Explainability and causability in digital pathology

https://doi.org/10.1002/cjp2.322
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1001/jamadermatol.2019.1735openalex-snapshot
Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition
inferredcites →https://doi.org/10.1002/path.5331openalex-snapshot
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inferredcites →https://doi.org/10.1002/path.5662openalex-snapshot
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inferredcites →https://doi.org/10.1002/path.5898openalex-snapshot
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The state of the art for artificial intelligence in lung digital pathology
inferredcites →https://doi.org/10.1002/widm.1312openalex-snapshot
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inferredcites →https://doi.org/10.1007/978-3-030-50402-1_4openalex-snapshot
Survey of XAI in Digital Pathology
inferredcites →https://doi.org/10.1007/978-3-030-65965-3_28openalex-snapshot
Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
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The Next Frontier: AI We Can Really Trust
inferredcites →https://doi.org/10.1007/978-3-030-99838-7_12openalex-snapshot
Digital and Computational Pathology: A Specialty Reimagined
inferredcites →https://doi.org/10.1007/978-3-031-04083-2_2openalex-snapshot
Explainable AI Methods - A Brief Overview
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inferredcites →https://doi.org/10.1016/j.compbiomed.2020.103865openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.future.2022.03.009openalex-snapshot
The explainability paradox: Challenges for xAI in digital pathology
inferredcites →https://doi.org/10.1016/j.ijhcs.2022.102839openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.inffus.2021.11.003openalex-snapshot
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inferredcites →https://doi.org/10.1093/ajcp/aqab085openalex-snapshot
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inferredcites →https://doi.org/10.1109/access.2021.3051315openalex-snapshot
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inferredcites →https://doi.org/10.1109/access.2022.3154776openalex-snapshot
Personas for Artificial Intelligence (AI) an Open Source Toolbox
inferredcites →https://doi.org/10.1109/icdm.2019.00036openalex-snapshot
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inferredcites →https://doi.org/10.1109/jproc.2021.3060483openalex-snapshot
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inferredcites →https://doi.org/10.1109/mc.2021.3092610openalex-snapshot
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inferredcites →https://doi.org/10.1111/cup.13954openalex-snapshot
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inferredcites →https://doi.org/10.1111/tbj.13858openalex-snapshot
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inferredcites →https://doi.org/10.1145/3233231openalex-snapshot
The Mythos of Model Interpretability
inferredcites →https://doi.org/10.1145/3287560.3287574openalex-snapshot
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inferredcites →https://doi.org/10.1145/3387166openalex-snapshot
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
inferredcites →https://doi.org/10.1177/2374289519873088openalex-snapshot
Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods
inferredcites →https://doi.org/10.1186/s12911-020-01332-6openalex-snapshot
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inferredcites →https://doi.org/10.1186/s13075-021-02716-3openalex-snapshot
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inferredcites →https://doi.org/10.1609/aimag.v40i2.2850openalex-snapshot
DARPA's Explainable Artificial Intelligence Program
inferredcites →https://doi.org/10.2196/23436openalex-snapshot
Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study
inferredcites →https://doi.org/10.3892/mmr.2014.2914openalex-snapshot
Ki67 is a promising molecular target in the diagnosis of cancer (Review)
inferredcites →https://doi.org/10.48550/arxiv.1806.10574openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.2102.13076openalex-snapshot
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Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/2056-4538.12366openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/2056-4538.70049openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.engappai.2024.109561openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.engmed.2026.100132openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.esmorw.2026.100737openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.ijhcs.2024.103315openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jbi.2024.104600openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.job.2025.100696openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jpi.2023.100347openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jpi.2024.100387openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jpi.2025.100454openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jpi.2026.100710openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.kint.2025.06.029openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.knosys.2025.113333openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.landig.2026.101017openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.measen.2024.101646openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.media.2025.103491openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.modpat.2025.100754openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.modpat.2025.100817openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.modpat.2025.100932openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.nbt.2024.12.003openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.path.2023.11.013openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41523-025-00857-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41568-024-00694-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41698-025-01065-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/neuonc/noag149openalex-snapshot
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inferred← citeshttps://doi.org/10.1097/pap.0000000000000536openalex-snapshot
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inferred← citeshttps://doi.org/10.1371/journal.pdig.0000391openalex-snapshot
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inferred← citeshttps://doi.org/10.1515/cclm-2023-1124openalex-snapshot
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inferred← citeshttps://doi.org/10.1631/jzus.a2500360openalex-snapshot
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inferred← citeshttps://doi.org/10.17816/cp11244openalex-snapshot
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inferred← citeshttps://doi.org/10.21203/rs.3.rs-3934081/v1openalex-snapshot
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inferred← citeshttps://doi.org/10.21203/rs.3.rs-5302339/v1openalex-snapshot
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inferred← citeshttps://doi.org/10.21203/rs.3.rs-9249572/v1openalex-snapshot
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inferred← citeshttps://doi.org/10.2147/bctt.s605985openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fmed.2023.1337335openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fneur.2024.1345687openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fonc.2023.1325271openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/a17060254openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/biomedinformatics4010012openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/diagnostics16111702openalex-snapshot
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inferred← citeshttps://doi.org/10.48550/arxiv.2409.03080openalex-snapshot
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inferred← citeshttps://doi.org/10.7180/kmj.24.140openalex-snapshot
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inferred← citeshttps://doi.org/10.7759/cureus.78155openalex-snapshot
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