Trustworthy Machine Learning for Healthcare [electronic resource] : First International Workshop, TML4H 2023, Virtual Event, May 4, 2023, Proceedings /

Do Tissue Source Sites leave identifiable Signatures in Whole Slide Images beyond staining? -- Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography -- Privacy-preserving machine learning for healthcare: open challenges and future perspectives -- Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolution. Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. Isabel Chien, Javier Gonzalez Hernandez, Richard E Turner -- CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs -- ExBEHRT: Extended Transformer for Electronic Health Records -- Stasis: Reinforcement Learning Simulators for Human-Centric Real-World Environments. Cross-domain Microscopy Cell Counting by Disentangled Transfer Learning -- Post-hoc Saliency Methods Fail to Capture Latent Feature Importance in Time Series Data -- Enhancing Healthcare Model Trustworthiness through Theoretically Guaranteed One-Hidden-Layer CNN Purification -- A Kernel Density Estimation based Quality Metric for Quality Assessment of Obstetric Ultrasound Video -- Learn2Agree: Fitting with Multiple Annotators without Objective Ground Truth -- Conformal Prediction Masks: Visualizing Uncertainty in Medical Imaging -- Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness -- Geometry-Based end-to-end Segmentation of Coronary artery ib Computed Tomography Angiograph.

This book constitutes the proceedings of First International Workshop, TML4H 2023, held virtually, in May 2023. The 16 full papers included in this volume were carefully reviewed and selected from 30 submissions. The goal of this workshop is to bring together experts from academia, clinic, and industry with an insightful vision of promoting trustworthy machine learning in healthcare in terms of scalability, accountability, and explainability.