Trustworthy AI in medical imaging:
Gespeichert in:
| Weitere beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
London
Elsevier, AP, Academic Press
[2025]
|
| Ausgabe: | 1st ed. |
| Schriftenreihe: | The Elsevier and MICCAI Society book series
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| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035416735&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | xxiii, 512 Seiten Illustrationen |
| ISBN: | 9780443237614 |
Internformat
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Contents List of contributors. xiii Preface.xxiii PART 1 Preliminaries_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ CHAPTER 1 Introduction to trustworthy Al for medical imaging. 3 Marco Lorenzi and Maria A. Zuluaga 1.1 Lecture plan. CHAPTER 2 References. 4 5 The fundamentals of Al ethics in medical imaging. 7 Julia Amann, Valerie K. Bürger, Michelle Livne, Cathrine K. T. Bui, and Vince I. Madai 2.1 Introduction. 2.2 From ethics to AI ethics, to responsible and trustworthy AI. 2.3 The need for trustworthy AI in medical imaging. 2.4 Addressing ethical challenges in practice. 2.5 Conclusion.
References. 7 7 10 20 25 26 PART 2 Robustness_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ CHAPTER 3 Machine learning robustness: a primer. 37 Houssem Ben Braiek and Foutse Khomh 3.1 Definition. 3.2 Challenges. 3.3 Robustness assessment. 3.4 Robustness amelioration. 3.5 Conclusion. References. CHAPTER 4 37 43 46 54 62 63 Navigating the unknown: out-of-distribution detection for medical imaging. 73 Moritz Fuchs, Anastasios Nikolas Angelopoulos, Magdalini Paschali, Christian Baumgartner, and Anirban Mukhopadhyay 4.1 Introduction. 4.2 Distribution shifts in medical
imaging. 73 75 V
vi Contents 4.3 4.4 4.5 4.6 4.7 4.8 CHAPTER 5 OOD detection workflow in medical imaging. OOD detection, open-set recognition, and beyond. OOD detection methods. Metrics and evaluation practices. Medical OOD detection benchmarks datasets. Conclusion. References. From out-of-distribution detection to quality control. 101 Benjamin Lambert, Florence Forbes, and Michel Dojat 5.1 Introduction. 5.2 A taxonomy of QC methods for ML-based medical image analysis. 5.3 Practical demonstration of a unified input-output QC for medical image segmentation. Ill 5.4 Conclusion. References. CHAPTER 6 76 78 81 87 89 90 91 101 103 119 119
Domain shift, domain adaptation, and generalization. 127 Jonas Richiardi, Veronica Ravano, Nataliia Molchanova, Pedro Μ. Gordaliza, Tobias Kober, and Meritxell Bach Cuadra 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 Introduction. Distributional shifts in medical imaging datasets. A brief introduction to MRI. Sources of heterogeneity and covariate shift in MRI. Quantifying domain shift. Domain adaptation and harmonization. Learning to generalize. Conclusions. References. 127 127 129 130 131 133 139 143 144 PART 3 Validation, transparency, and reproducibility CHAPTER 7 7.1 7.2 7.3 7.4 7.5 Fundamentals on transparency, reproducibility, and validation. 155 Sorina Camarasu-Pop, Gael Vila, Tristan Glatard, Axel Bonnet, Carole Frindel, and Hélène Ratiney Introduction.
Definitions. Analyses. Software tools. Data. 155 155 160 164 166
Contents 7.6 Discussion. Acknowledgments. References. vii 168 171 172 CHAPTER 8 Reproducibility in medical image computing: what is it and how is it assessed?. 177 Olivier Colliot, Elina Thibeau-Sutre, Camille Brianceau, and Ninon Burgos 8.1 Introduction. 177 8.2 Conceptual framework. 178 8.3 The building blocks of reproducibility. 183 8.4 Assessment of reproducibility at MICCAI. 186 8.5 Where do we stand?. 193 8.6 Where could we go?. 195 Acknowledgments. 200 Appendix 8.A Reproducibility in medical image computing. 200
References. 201 Collaborative evaluation forperformance assessment of medical imaging applications. 205 CHAPTER 9 Hasan Kassem, Akshita Singh, Alejandro Aristizabal, Spyridon Bakas, Micah Shelter, Renato Umeton, Sarthak Pati, Prakash Narayana Moorthy, and Alexandros Karargyris 9.1 From bench to bedside. 205 9.2 The importance of validation in medical imaging application lifecycle. 206 208 9.4 Technology for collaborative evaluation. 214 9.5 Case study: real-world collaborative evaluation of a medical imaging application. 217 9.6 Further discussion: challenges and opportunities. 218 References. 219 9.3 Collaborative evaluation. CHAPTER 10 Challenges as a framework for trustworthy Al. 223 Michela Antonelli, Keyvan Farahani, Carole H. Sudre, Μ. Jorge Cardoso, Lena Maier-Hein, and Annika Reinke 10.1 10.2 10.3 10.4 10.5 10.6
Introduction. Datasets. Metrics and rankingsin challenges. Best practicesin challenge organization. Open issues. Summary. References. 223 225 228 233 238 241 242
viii Contents PART 4 Bias and fairness_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ CHAPTER 11 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 CHAPTER 12 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 Bias and fairness. 247 Andrew P. King and Tiarna Lee Introduction. 247 Why is fair AI needed now?. 247 Definitions and terminology. 250 What is fair AI?. 251 Fairness in medicine and medical AI. 252 Assessing bias. 254 Addressing bias. 256 Fair AI resources/initiatives. 258 Summary, conclusions, and future directions. 260 References. 260 Open challenges on fairness of artificial intelligence in medical imaging
applications. 265 Enzo Ferrante and Rodrigo Echeveste Introduction. 265 Why is my system biased?. 265 Biased metrics may result in misleading fairness audits. 268 Fighting against the leveling down effect. 270 Is it biased or is it just more difficult?. 271 Discovering biases in unseen populations. 272 Explaining biases beyond standard demographic attributes. 272 Conclusion. 274 References. 274 PART 5 Explainability, interpretability, and causality CHAPTER 13 13.1 13.2 13.3 13.4 13.5 13.6 Fundamentals on explainable and interpretable artificial intelligence models. Pietro Ducange, Francesco Marcelloni, Alessandro Renda, and Fabrizio Ruffini Introduction.
Preliminaries. Explainable and interpretable models by-design. Post-hoc methods for explaining black-boxes. Examples of usage. Summary and concluding remarks. Acknowledgment. References. 279 279 280 283 287 290 293 294 294
Contents CHAPTER 14 Causality: fundamental principles and tools. ix 297 Irene Balelli, Safaa Al-Ali, Elise Dumas, and Judith Abecassis 14.1 Introduction. 14.2 The basis for building a causal reasoning. 14.3 Causal discovery or the query for the data generating process. 14.4 Causal inference and counterfactual questions. 14.5 Conclusions. Acknowledgments. References. 297 298 301 307 312 313 313 CHAPTER 15 interpretable Al for medical image analysis: methods, evaluation, and clinical considerations. 315 Tiago Gonçalves, Anna Hedström, Aurélie Pahud de Mortanges, Xiaoxiao Li, Henning Müller, Jaime S. Cardoso, and Mauricio Reyes 15.1 Introduction. 315 15.2 Interpretable methods. 316 15.3 Evaluating interpretable and explainable AI methods. 329 334 15.5
Discussion. 336 References. 338 15.4 Clinical consideration to bring XAI methods closer to the clinics. CHAPTER 16 Explainable Al for medical image analysis. 347 Carolina Bras, Helena Montenegro, Leon Y. Cai, Valentina Corbetta, Yuankai Huo, Wilson Silva, Jaime S. Cardoso, Bennett A. Landman, and Ivana Isgum 16.1 Introduction. 347 348 Example-based explanations. 354 Textual explanations. 357 Concept-based explanations. 359 Discussion. 361 References. 362 16.2 Visual explanations. 16.3 16.4 16.5 16.6 CHAPTER 17 Causal reasoning in medicalimaging. 367 Athanasios Vlontzos, Christine Muller, and Bernhard Kainz 17.1 17.2 17.3 17.4 17.5
Introduction. Background. Causal discovery in medical imaging. Causal inference in medical imaging. Discussion and conclusion. References. 367 369 371 373 376 377
X Contents PART 6 Privacy-preserving machine learning CHAPTER 18 Fundamentals of privacy-preserving and secure machine learning. 385 Aftab Akram, Pascal Zimmer, Clémentine Gritti, Ghassan Кагате, and Melek Önen 18.1 18.2 18.3 18.4 18.5 18.6 Introduction. Privacy attacks. Privacy defenses. Security attacks. Security defenses. Conclusion. References. 385 386 389 395 401 403 404 CHAPTER 19 Differential privacy in medical imaging applications. 411 Johannes Kaiser, Tamara Mueller, and Georgios Kaissis 19.1 19.2 19.3 19.4 19.5 19.6 19.7 Introduction. Mathematical background. Properties of
DP. Differential privacy in deep learning. Challenges of differential privacy. Applications of differential privacyin medical imaging. Conclusion. References. 411 415 417 418 419 420 421 422 PART 7 Collaborative learning CHAPTER 20 Collaborative learning. 427 Lucia Innocenti, Michela Antonelli, Sebastien Ourselin, and Marco Lorenzi 20.1 20.2 20.3 20.4 20.5 CHAPTER 21 Introduction. Limitations of centralized learning. The opportunity of collaborative learning. A dive into collaborative learning paradigms. Discussion. References. 427 428 429 430 437 437 Large-scale
collaborative studies in medical imaging through meta-analyses. 441 Andre Altmann, Jérôme Dockès, Paul Thompson, and Neda Jahanshad 21.1 Introduction. 441 21.2 Methods for meta-analysis. 442
Contents xi 21.3 Meta-analysis and biomedical data. 445 21.4 Limits of meta-analysis and future directions. 451 References. 452 CHAPTER 22 Promises and open challenges for translating federated learning in hospital environments. 455 Olivier Humbert, Hugo Crochet, and Renaud Schiappa 22.1 Introduction. 455 22.2 Advantages of federated learning in healthcare. 457 22.3 Issues of federated learning in healthcare. 462 465 References. 466 22.4 Conclusions. PART 8 Beyond the technical aspects CHAPTER 23 Stakeholder engagement: the path to trustworthy Al in healthcare. 471 Marina Camacho, Joan Perramon, Xènia Puig-Bosch, Vien Ngoc Dang, Oliver Diaz, and Karim Lekadir References. 471 472 475 479 482 489 491
Index. 495 23.1 Introduction. 23.2 The stakeholder landscape. 23.3 Methods of stakeholder engagement. 23.4 Guidelines for effective engagement. 23.5 Literature review on stakeholder engagement in AI for healthcare. 23.6 Conclusion. |
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| spelling | Trustworthy AI in medical imaging edited by Marco Lorenzi, Maria A. Zuluaga London Elsevier, AP, Academic Press [2025] xxiii, 512 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier The Elsevier and MICCAI Society book series Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Bildgebendes Verfahren (DE-588)4006617-4 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s Bildgebendes Verfahren (DE-588)4006617-4 s DE-604 Lorenzi, Marco edt Zuluaga, Maria A. edt Erscheint auch als Online-Ausgabe 978-0-443-23760-7 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035416735&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Trustworthy AI in medical imaging Künstliche Intelligenz (DE-588)4033447-8 gnd Bildgebendes Verfahren (DE-588)4006617-4 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4006617-4 |
| title | Trustworthy AI in medical imaging |
| title_auth | Trustworthy AI in medical imaging |
| title_exact_search | Trustworthy AI in medical imaging |
| title_full | Trustworthy AI in medical imaging edited by Marco Lorenzi, Maria A. Zuluaga |
| title_fullStr | Trustworthy AI in medical imaging edited by Marco Lorenzi, Maria A. Zuluaga |
| title_full_unstemmed | Trustworthy AI in medical imaging edited by Marco Lorenzi, Maria A. Zuluaga |
| title_short | Trustworthy AI in medical imaging |
| title_sort | trustworthy ai in medical imaging |
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