Jay B. Patel

According to our database1, Jay B. Patel authored at least 18 papers between 2019 and 2023.

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

Timeline

Legend:

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In proceedings 
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PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction.
IEEE Trans. Medical Imaging, 2023

A Deep Learning Based Framework for Joint Image Registration and Segmentation of Brain Metastases on Magnetic Resonance Imaging.
Proceedings of the Machine Learning for Healthcare Conference, 2023

2022
Is this good enough? On expert perception of brain tumor segmentation quality.
Proceedings of the Medical Imaging 2022: Image Perception, 2022

Do I know this? segmentation uncertainty under domain shift.
Proceedings of the Medical Imaging 2022: Image Processing, 2022

2021
DeepNeuro: an open-source deep learning toolbox for neuroimaging.
Neuroinformatics, 2021

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results.
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CoRR, 2021

Addressing catastrophic forgetting for medical domain expansion.
CoRR, 2021

Opportunities and Challenges for Deep Learning in Brain Lesions.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021

2020
A framework to analyze decision strategies for multi-band spectrum sensing in cognitive radios.
Phys. Commun., 2020

The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions.
CoRR, 2020

Towards Trainable Saliency Maps in Medical Imaging.
CoRR, 2020

Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging.
CoRR, 2020

Assessing the validity of saliency maps for abnormality localization in medical imaging.
CoRR, 2020

An exploration of uncertainty information for segmentation quality assessment.
Proceedings of the Medical Imaging 2020: Image Processing, 2020


Radiomics and Radiogenomics with Deep Learning in Neuro-oncology.
Proceedings of the Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, 2020

Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI Using Selective Kernel Networks.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020

2019
Give me (un)certainty - An exploration of parameters that affect segmentation uncertainty.
CoRR, 2019


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