Abhijit Guha Roy

Orcid: 0000-0001-7221-468X

According to our database1, Abhijit Guha Roy authored at least 43 papers between 2015 and 2024.

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Bibliography

2024
MINT: A wrapper to make multi-modal and multi-image AI models interactive.
CoRR, 2024

2023
Conformal prediction under ambiguous ground truth.
Trans. Mach. Learn. Res., 2023

Evaluating AI systems under uncertain ground truth: a case study in dermatology.
CoRR, 2023

Generative models improve fairness of medical classifiers under distribution shifts.
CoRR, 2023

2022
Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions.
Medical Image Anal., 2022

Robust and Efficient Medical Imaging with Self-Supervision.
CoRR, 2022

2021
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection.
CoRR, 2021

2020
Recalibrating 3D ConvNets With Project & Excite.
IEEE Trans. Medical Imaging, 2020

'Squeeze & excite' guided few-shot segmentation of volumetric images.
Medical Image Anal., 2020

Contrastive Training for Improved Out-of-Distribution Detection.
CoRR, 2020

Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers.
Proceedings of the Machine Learning in Medical Imaging - 11th International Workshop, 2020

Importance Driven Continual Learning for Segmentation Across Domains.
Proceedings of the Machine Learning in Medical Imaging - 11th International Workshop, 2020

2019
Recalibrating Fully Convolutional Networks With Spatial and Channel "Squeeze and Excitation" Blocks.
IEEE Trans. Medical Imaging, 2019

QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy.
NeuroImage, 2019

Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control.
NeuroImage, 2019

BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning.
CoRR, 2019

Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness.
CoRR, 2019

'Project & Excite' Modules for Segmentation of Volumetric Medical Scans.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

An AutoML Approach for the Prediction of Fluid Intelligence from MRI-Derived Features.
Proceedings of the Adolescent Brain Cognitive Development Neurocognitive Prediction, 2019

Prediction of Fluid Intelligence from T1-Weighted Magnetic Resonance Images.
Proceedings of the Adolescent Brain Cognitive Development Neurocognitive Prediction, 2019

3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness.
Proceedings of the Information Processing in Medical Imaging, 2019

2018
Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks.
CoRR, 2018

SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis.
CoRR, 2018

QuickNAT: Segmenting MRI Neuroanatomy in 20 seconds.
CoRR, 2018

Concurrent Spatial and Channel 'Squeeze & Excitation' in Fully Convolutional Networks.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018

Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018

Learning Optimal Deep Projection of <sup>18</sup>F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes.
Proceedings of the Deep Learning in Medical Image Analysis - and - Multimodal Learning for Clinical Decision Support, 2018

InfiNet: Fully convolutional networks for infant brain MRI segmentation.
Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, 2018

Multiple instance learning of deep convolutional neural networks for breast histopathology whole slide classification.
Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, 2018

Abstract: Fast MRI Whole Brain Segmentation with Fully Convolutional Neural Networks.
Proceedings of the Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme, 2018

Abstract: Deep Hashing for Large-Scale Medical Image Retrieval.
Proceedings of the Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme, 2018

2017
ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network.
CoRR, 2017

Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, 2017

Hashing with Residual Networks for Image Retrieval.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, 2017

Classifying histopathology whole-slides using fusion of decisions from deep convolutional network on a collection of random multi-views at multi-magnification.
Proceedings of the 14th IEEE International Symposium on Biomedical Imaging, 2017

2016
Lumen Segmentation in Intravascular Optical Coherence Tomography Using Backscattering Tracked and Initialized Random Walks.
IEEE J. Biomed. Health Informatics, 2016

Supervised domain adaptation of decision forests: Transfer of models trained in vitro for in vivo intravascular ultrasound tissue characterization.
Medical Image Anal., 2016

Deep Residual Hashing.
CoRR, 2016

Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography.
Proceedings of the 13th IEEE International Symposium on Biomedical Imaging, 2016

Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography.
Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016

2015
Bag of forests for modelling of tissue energy interaction in optical coherence tomography for atherosclerotic plaque susceptibility assessment.
Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, 2015

DASA: Domain adaptation in stacked autoencoders using systematic dropout.
Proceedings of the 3rd IAPR Asian Conference on Pattern Recognition, 2015


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