Cover image for Trustworthy Machine Learning for Healthcare First International Workshop, TML4H 2023, Virtual Event, May 4, 2023, Proceedings
Title:
Trustworthy Machine Learning for Healthcare First International Workshop, TML4H 2023, Virtual Event, May 4, 2023, Proceedings
Author:
Chen, Hao. editor. (orcid)
ISBN:
9783031395390
Edition:
1st ed. 2023.
Physical Description:
X, 198 p. 68 illus., 60 illus. in color. online resource.
Series:
Lecture Notes in Computer Science, 13932
Contents:
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.
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