Daniel C. Castro

Orcid: 0000-0002-6829-7045

According to our database1, Daniel C. Castro authored at least 33 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Rethinking Fair Representation Learning for Performance-Sensitive Tasks.
CoRR, 2024

An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation.
CoRR, 2024

MAIRA-2: Grounded Radiology Report Generation.
CoRR, 2024

Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology.
CoRR, 2024

RAD-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision.
CoRR, 2024

Causal Modelling Agents: Causal Graph Discovery through Synergising Metadata- and Data-driven Reasoning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology.
Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024

MAIRA at RRG24: A specialised large multimodal model for radiology report generation.
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, 2024

2023
RadEdit: stress-testing biomedical vision models via diffusion image editing.
CoRR, 2023

MAIRA-1: A specialised large multimodal model for radiology report generation.
CoRR, 2023

No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging.
CoRR, 2023

Measuring axiomatic soundness of counterfactual image models.
Proceedings of the Eleventh International Conference on Learning Representations, 2023


Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Analysing the effectiveness of a generative model for semi-supervised medical image segmentation.
Proceedings of the Machine Learning for Health, 2022

Deep Structural Causal Shape Models.
Proceedings of the Computer Vision - ECCV 2022 Workshops, 2022

Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
Active label cleaning: Improving dataset quality under resource constraints.
CoRR, 2021

Hierarchical Analysis of Visual COVID-19 Features from Chest Radiographs.
CoRR, 2021

2020
Deep Structural Causal Models for Tractable Counterfactual Inference.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Image-Level Harmonization of Multi-site Data Using Image-and-Spatial Transformer Networks.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

2019
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning.
J. Mach. Learn. Res., 2019

Causality matters in medical imaging.
CoRR, 2019

Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects.
CoRR, 2019

Domain Generalization via Model-Agnostic Learning of Semantic Features.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing.
Proceedings of the Information Processing in Medical Imaging, 2019

2018
Contextual Face Recognition with a Nested-Hierarchical Nonparametric Identity Model.
CoRR, 2018

Cardiac MR Segmentation from Undersampled k-space Using Deep Latent Representation Learning.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018

Bayesian Deep Learning for Accelerated MR Image Reconstruction.
Proceedings of the Machine Learning for Medical Image Reconstruction, 2018

Nonparametric Density Flows for MRI Intensity Normalisation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018

Semi-Supervised Learning via Compact Latent Space Clustering.
Proceedings of the 35th International Conference on Machine Learning, 2018

From Face Recognition to Models of Identity: A Bayesian Approach to Learning About Unknown Identities from Unsupervised Data.
Proceedings of the Computer Vision - ECCV 2018, 2018


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