Julius Chapiro

According to our database1, Julius Chapiro authored at least 10 papers between 2017 and 2021.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2021
Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration.
Medical Image Anal., 2021

2020
Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification.
IEEE Trans. Medical Imaging, 2020

Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-domain Liver Segmentation.
Proceedings of the Interpretable and Annotation-Efficient Learning for Medical Image Computing, 2020

Cross-Modality Segmentation by Self-supervised Semantic Alignment in Disentangled Content Space.
Proceedings of the Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, 2020

Automatic Multimodal Registration via Intraprocedural Cone-Beam CT Segmentation using MRI Distance Maps.
Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020

2019
Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making.
CoRR, 2019

Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

Domain-Agnostic Learning With Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019

2018
Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework.
Proceedings of the Patch-Based Techniques in Medical Imaging, 2018

2017
Liver Tissue Classification in Patients with Hepatocellular Carcinoma by Fusing Structured and Rotationally Invariant Context Representation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, 2017


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