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Online Ressourcen
Link zu diesem Datensatz https://d-nb.info/1400446252
Titel Fairness of AI in Medical Imaging : Third International Workshop, FAIMI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings / edited by Esther Puyol-Antón, Enzo Ferrante, Aasa Feragen, Andrew King, Veronika Cheplygina, Melani Ganz-Benjaminsen, Ben Glocker, Eike Petersen, Heisook Lee
Person(en) Puyol-Antón, Esther (Herausgeber)
Ferrante, Enzo (Herausgeber)
Feragen, Aasa (Herausgeber)
King, Andrew (Herausgeber)
Cheplygina, Veronika (Herausgeber)
Ganz-Benjaminsen, Melani (Herausgeber)
Glocker, Ben (Herausgeber)
Petersen, Eike (Herausgeber)
Lee, Heisook (Herausgeber)
Organisation(en) SpringerLink (Online service) (Sonstige)
Ausgabe 1st ed. 2026
Verlag Cham : Springer Nature Switzerland, Imprint: Springer
Zeitliche Einordnung Erscheinungsdatum: 2026
Umfang/Format Online-Ressource, XI, 220 p. 62 illus., 59 illus. in color. : online resource.
Andere Ausgabe(n) Printed edition:: ISBN: 978-3-032-05869-0
Printed edition:: ISBN: 978-3-032-05871-3
Inhalt -- LTCXNet: Tackling Long-Tailed Multi-Label Classification and Racial Bias in Chest X-Ray Analysis. -- Fairness and Robustness of CLIP-Based Models for Chest X-rays. -- ShortCXR: Benchmarking Self-Supervised Learning Methods for Shortcut Mitigation in Chest X-Ray Interpretation. -- How Fair Are Foundation Models? Exploring the Role of Covariate Bias in Histopathology. -- The Cervix in Context: Bias Assessment in Preterm Birth Prediction. -- Identifying Gender-Specific Visual Bias Signals in Skin Lesion Classification. -- Fairness-Aware Data Augmentation for Cardiac MRI using Text-Conditioned Diffusion Models. -- Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density classification. -- Does a Rising Tide Lift All Boats? Bias Mitigation for AI-based CMR Segmentation. -- MIMM-X: Disentangeling Spurious Correlations for Medical Image Analysis. -- Predicting Patient Self-reported Race From Skin Histological Images with Deep Learning. -- Robustness and sex differences in skin cancer detection: logistic regression vs CNNs. -- Sex-based Bias Inherent in the Dice Similarity Coefficient: A Model Independent Analysis for Multiple Anatomical Structures. -- The Impact of Skin Tone Label Granularity on the Performance and Fairness of AI Based Dermatology Image Classification Models. -- Causal Representation Learning with Observational Grouping for CXR Classification. -- Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer’s Disease Classification. -- Fair Dermatological Disease Diagnosis through Auto-weighted Federated Learning and Performance-aware Personalization. -- Assessing Annotator and Clinician Biases in an Open-Source-Based Tool Used to Generate Head CT Segmentations for Deep Learning Training. -- meval: A Statistical Toolbox for Fine-Grained Model Performance Analysis. -- Revisiting the Evaluation Bias Introduced by Frame Sampling Strategies in Surgical Video Segmentation Using SAM2. -- Disentanglement and Assessment of Shortcuts in Ophthalmological Retinal Imaging Exams
Persistent Identifier URN: urn:nbn:de:101:1-2605220435326.516891386294
DOI: 10.1007/978-3-032-05870-6
URL https://doi.org/10.1007/978-3-032-05870-6
ISBN/Einband/Preis 978-3-032-05870-6
Sprache(n) Englisch (eng)
Beziehungen Lecture Notes in Computer Science ; 15976
Anmerkungen Hinweis zur Barrierefreiheit: PDF/UA-1
Hinweis zur Barrierefreiheit: Table of contents navigation
Hinweis zur Barrierefreiheit: Single logical reading order
Hinweis zur Barrierefreiheit: Short alternative textual descriptions
Hinweis zur Barrierefreiheit: Use of color is not sole means of conveying information
Hinweis zur Barrierefreiheit: Use of high contrast between text and background color
Hinweis zur Barrierefreiheit: Next / Previous structural navigation
Hinweis zur Barrierefreiheit: All non-decorative content supports reading without sight
Hinweis zur Barrierefreiheit: Accessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupportspringernature.com. Please note that a more accessible version of this eBook is available as ePub
Hinweis zur Barrierefreiheit: No reading system accessibility options actively disabled
Hinweis zur Barrierefreiheit: Publisher contact for further accessibility information: accessibilitysupportspringernature.com
DDC-Notation 616.075 (maschinell ermittelte DDC-Kurznotation)
Sachgruppe(n) 610 Medizin, Gesundheit

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