Table of Contents

• Contrastive Learning of Medical Visual Representations from Paired Images and TextYuhao Zhang 0004, Hang Jiang, Yasuhide Miura, Christopher D. Manning, Curtis P. Langlotz. 2-25 [doi]
• Unified Auto Clinical Scoring (Uni-ACS) with Interpretable ML modelsAnthony Li, Ming Lun Ong, Chien Wei Oei, Weixiang Lian, Hwee Pin Phua, Lin Htun Htet, Wei Yen Lim. 26-53 [doi]
• Deep Cascade Learning for Optimal Medical Image Feature RepresentationJunwen Wang, Xin Du, Katayoun Farrahi, Mahesan Niranjan. 54-78 [doi]
• Survival Seq2Seq: A Survival Model based on Sequence to Sequence ArchitectureEbrahim Pourjafari, Navid Ziaei, Mohammad R. Rezaei, Amir Sameizadeh, Mohammad Shafiee, Mohammad Alavinia, Mansour Abolghasemian, Nick Sajadi. 79-100 [doi]
• Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical DataZepeng Huo, Xiaoning Qian, Shuai Huang, Zhangyang Wang, Bobak J. Mortazavi. 101-122 [doi]
• Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of SepsisShigehiko Schamoni, Michael Hagmann, Stefan Riezler. 123-145 [doi]
• Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in MedicineTheresa Blümlein, Joel Persson, Stefan Feuerriegel. 146-171 [doi]
• GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram PredictionJiacheng Zhu, Jielin Qiu, Zhuolin Yang, Douglas Weber, Michael A. Rosenberg, Emerson Liu, Bo Li 0026, Ding Zhao. 172-197 [doi]
• HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD CodingWeiming Ren, Ruijing Zeng, Tongzi Wu, Tianshu Zhu, Rahul G. Krishnan. 198-223 [doi]
• Survival Mixture Density NetworksXintian Han, Mark Goldstein, Rajesh Ranganath. 224-248 [doi]
• Latent Temporal Flows for Multivariate Analysis of Wearables DataMagda Amiridi, Gregory Darnell, Sean Jewell. 249-269 [doi]
• An hybrid CNN-Transformer model based on multi-feature extraction and attention fusion mechanism for cerebral emboli classificationYamil Vindas, Blaise Kévin Guépié, Marilys Almar, Emmanuel Roux, Philippe Delachartre. 270-296 [doi]
• EMIXER: End-to-end Multimodal X-ray Generation via Self-supervisionSiddharth Biswal, Peiye Zhuang, Ayis Pyrros, Nasir Siddiqui, Sanmi Koyejo, Jimeng Sun 0001. 297-324 [doi]
• Diagnosing Epileptogenesis with Deep Anomaly DetectionAmr Farahat, Diyuan Lu, Sebastian Bauer, Valentin Neubert, Lara Sophie Costard, Felix Rosenow, Jochen Triesch. 325-342 [doi]
• Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine LearningTrenton Chang, Michael W. Sjoding, Jenna Wiens. 343-390 [doi]
• EHR Safari: Data is ContextualWilliam Boag, Mercy Oladipo, Peter Szolovits. 391-408 [doi]
• A Multi Instance Learning Approach for Critical View of Safety Detection in Laparoscopic CholecystectomyYariv Colbeci, Maya Zohar, Daniel Neimark, Dotan Asselmann, Omri Bar. 409-424 [doi]
• Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory ModelsAlain Ryser, Laura Manduchi, Fabian Laumer, Holger Michel, Sven Wellmann, Julia E. Vogt. 425-458 [doi]
• How fair is your graph? Exploring fairness concerns in neuroimaging studiesFernanda Ribeiro, Valentina Shumovskaia, Thomas Davies, Ira Ktena. 459-478 [doi]
• MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray imagesNasir Hayat, Krzysztof J. Geras, Farah E. Shamout. 479-503 [doi]
• Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing MethodsRicards Marcinkevics, Ece Ozkan, Julia E. Vogt. 504-536 [doi]
• ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential EquationsAsem Alaa, Erik Mayer, Mauricio Barahona. 537-564 [doi]
• Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent ImagesNazanin Moradinasab, Yash Sharma 0002, Laura S. Shankman, Gary K. Owens, Donald E. Brown. 565-584 [doi]
• auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event DataChirag Nagpal, Willa Potosnak, Artur Dubrawski. 585-608 [doi]
• AudiFace: Multimodal Deep Learning for Depression ScreeningRicardo Flores, M. L. Tlachac, Ermal Toto, Elke A. Rundensteiner. 609-630 [doi]
• Reinforcement Learning For Sepsis Treatment: A Continuous Action Space SolutionYong Huang, Rui Cao, Amir-Mohammad Rahmani. 631-647 [doi]
• Learning Optimal Summaries of Clinical Time-series with Concept Bottleneck ModelsCarissa Wu, Sonali Parbhoo, Marton Havasi, Finale Doshi-Velez. 648-672 [doi]
• Classifying Unstructured Clinical Notes via Automatic Weak SupervisionChufan Gao, Mononito Goswami, Jieshi Chen, Artur Dubrawski. 673-690 [doi]
• KCRL: A Prior Knowledge Based Causal Discovery Framework with Reinforcement LearningUzma Hasan, Md Osman Gani. 691-714 [doi]
• Error Amplification When Updating Deployed Machine Learning ModelsGeorge-Alexandru Adam, Chun-Hao Kingsley Chang, Benjamin Haibe-Kains, Anna Goldenberg. 715-740 [doi]
• Development and Validation of ML-DQA - a Machine Learning Data Quality Assurance Framework for HealthcareMark P. Sendak, Gaurav Sirdeshmukh, Timothy Ochoa, Hayley Premo, Linda Tang, Kira Niederhoffer, Sarah Reed, Kaivalya Deshpande, Emily Sterrett, Melissa Bauer, Laurie Snyder, Afreen Shariff, David Whellan, Jeffrey Riggio, David Gaieski, Kristin Corey, Megan Richards, Michael Gao, Marshall Nichols, Bradley Heintze, William Knechtle, William Ratliff, Suresh Balu. 741-759 [doi]
• Reducing Reliance on Spurious Features in Medical Image Classification with Spatial SpecificityKhaled Saab, Sarah M. Hooper, Mayee F. Chen, Michael Zhang, Daniel Rubin, Christopher Ré. 760-784 [doi]
• Searching for Fine-Grained Queries in Radiology Reports Using Similarity-Preserving Contrastive EmbeddingTanveer F. Syeda-Mahmood, Luyao Shi. 785-799 [doi]
• SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) predictionIntae Moon, Stefan Groha, Alexander Gusev. 800-827 [doi]
• Why predicting risk can't identify 'risk factors': empirical assessment of model stability in machine learning across observational health databasesAniek F. Markus, Peter R. Rijnbeek, Jenna Marie Reps. 828-852 [doi]
• Few-Shot Learning with Semi-Supervised Transformers for Electronic Health RecordsRaphael Poulain, Mehak Gupta, Rahmatollah Beheshti. 853-873 [doi]
• Evaluating Uncertainty-Based Deep Learning Explanations for Prostate Lesion DetectionChristopher M. Trombley, Mehmet Akif Gulum, Merve Ozen, Enes Esen, Melih Aksamoglu, Mehmed M. Kantardzic. 874-891 [doi]
• ALGES: Active Learning with Gradient Embeddings for Semantic Segmentation of Laparoscopic Surgical ImagesJosiah Aklilu, Serena Yeung. 892-911 [doi]