Interpretability of machine intelligence in medical image computing : 5th international workshop, iMIMIC 2022, held in conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, proceedings / / edited by Mauricio Reyes, Pedro Henriques Abreu, and Jaime Cardoso

• Pubblicazione: Singapore : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (134 pages)
• Disciplina: 616.0754
• Soggetto topico: Computer-assisted surgery; Diagnostic imaging - Data processing
• Persona (resp. second.): Henriques AbreuPedro; CardosoJaime; ReyesMauricio
• Note generali: Includes index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Interpretable Lung Cancer Diagnosis with Nodule Attribute Guidance and Online Model Debugging -- 1 Introduction -- 2 Materials -- 3 Methodology -- 3.1 Collaborative Model Architecture with Attribute-Guidance -- 3.2 Debugging Model with Semantic Interpretation -- 3.3 Explanation by Attribute-Based Nodule Retrieval -- 4 Experiments and Results -- 4.1 Implementation -- 4.2 Quantitative Evaluation -- 4.3 Trustworthiness Check and Interpretable Diagnosis -- 5 Conclusions -- References -- Do Pre-processing and Augmentation Help Explainability? A Multi-seed Analysis for Brain Age Estimation -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Results -- 4.1 Performance -- 4.2 Voxel Agreement -- 4.3 Atlas-Based Analyses -- 4.4 Region Validation -- 5 Conclusion -- References -- Towards Self-explainable Transformers for Cell Classification in Flow Cytometry Data -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Architecture -- 3.2 Preprocessing -- 3.3 Loss Function -- 3.4 Data Augmentation -- 4 Experiments -- 4.1 Data -- 4.2 Results -- 5 Conclusion -- References -- Reducing Annotation Need in Self-explanatory Models for Lung Nodule Diagnosis -- 1 Introduction -- 2 Method -- 3 Experimental Results -- 3.1 Prediction Performance of Nodule Attributes and Malignancy -- 3.2 Analysis of Extracted Features in Learned Space -- 3.3 Ablation Study -- 4 Conclusion -- References -- Attention-Based Interpretable Regression of Gene Expression in Histology -- 1 Introduction -- 2 Methods -- 2.1 Datasets -- 2.2 Multiple Instance Regression of Gene Expression -- 2.3 Attention-Based Model Interpretability -- 2.4 Evaluation of Performance and Interpretability -- 3 Experiments and Results -- 3.1 Network Training -- 3.2 Quantitative Model Evaluation -- 3.3 Attention-Based Identification of Hotspots and Patterns. ; 3.4 Quantitative Evaluation of the Attention -- 4 Discussion -- 5 Conclusion -- A Description of Selected Genes -- B Detailed Model Evaluation -- C Additional Visualizations -- D Single-Cell Co-expression -- References -- Beyond Voxel Prediction Uncertainty: Identifying Brain Lesions You Can Trust -- 1 Introduction -- 2 Our Framework: Graph Modelization for Lesion Uncertainty Quantification -- 2.1 Monte Carlo Dropout Model and Voxel-Wise Uncertainty -- 2.2 Graph Dataset Generation -- 2.3 GCNN Architecture and Training -- 3 Material and Method -- 3.1 Data -- 3.2 Comparison with Known Approaches -- 3.3 Evaluation Setting -- 3.4 Implementation Details -- 4 Results and Discussion -- 5 Conclusion -- References -- Interpretable Vertebral Fracture Diagnosis -- 1 Introduction -- 1.1 Related Work -- 2 Methodology -- 2.1 Vertebral Fracture Detection -- 2.2 Semantic Concept Extraction (Correlation) -- 2.3 Visualization of Highly Correlating Concepts at Inference -- 3 Experimental Setup -- 4 Results and Discussion -- 4.1 Clinical Meaningfulness of Extracted Semantic Concepts -- 4.2 Single-Inference Concept Visualization -- 5 Conclusion -- References -- Multi-modal Volumetric Concept Activation to Explain Detection and Classification of Metastatic Prostate Cancer on PSMA-PET/CT -- 1 Introduction -- 2 Data -- 3 Method -- 3.1 Preprocessing -- 3.2 Detection -- 3.3 Classification -- 3.4 Explainable AI -- 4 Results -- 4.1 Detection -- 4.2 Classification -- 4.3 Explainable AI -- 5 Discussion -- 6 Conclusion -- References -- KAM - A Kernel Attention Module for Emotion Classification with EEG Data -- 1 Introduction -- 2 Related Work -- 3 Kernel Attention Module -- 4 Experiments -- 5 Conclusion -- References -- Explainable Artificial Intelligence for Breast Tumour Classification: Helpful or Harmful -- 1 Introduction -- 2 Related Work -- 2.1 XAI in Medicine. ; 3 Model Setup -- 3.1 Data Pre-Processing -- 3.2 Model Architecture -- 4 Explanations -- 4.1 LIME -- 4.2 RISE -- 4.3 SHAP -- 5 Evaluating Explanations -- 5.1 One-Way ANOVA -- 5.2 Kendall's Tau -- 5.3 Radiologist Evaluation -- 5.4 Threats to Validity -- 6 Observations and Discussion -- 6.1 Discussion -- A Appendix -- A.1 Model Training Results -- A.2 Choosing L Parameter for LIME -- A.3 One-Way ANOVA Results -- A.4 Pixel Agreement Statistics -- A.5 Ranked Biased Overlap (RBO) Results -- A.6 Kendall's Tau Results -- A.7 Radiologist Opinions -- A.8 Explanation Examples -- References -- Author Index.
• Titolo autorizzato: Interpretability of machine intelligence in medical image computing
• ISBN: 3-031-17976-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996495570803316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science