News
- Feb 2021: Received the Stanford Bio-X Travel award!
- Feb 2021: Gave a guest lecture in the Advanced Data Mining class at Utah State University
- Jan 2021: Two papers accepted at ISBI 2021!
- Jan 2021: Presented our Progressive adversarial semantic segmentation paper at ICPR 2020
- Dec 2020: Presented our Partly supervised multi-task learning paper at ICMLA 2020 [Slides]
- Oct 2020: Gave a guest lecture in the Deep Learning class at New Mexico State University
- Oct 2020: Got selected for the 2020 AAPM Expanding Horizons Travel Grant award!
- Sep 2020: Gave an invited talk on Emerging Biomedical Imaging Technologies at RUET
- Sep 2020: Full paper accepted at ICMLA 2020
Old News
- Aug 2020: Got featured on DiscoverPhDs
- Jul 2020: Presented our scoliosis analysis paper at CBMS 2020
- Jul 2020: Joined Stanford as a Postdoc
- Jun 2020: Paper accepted at ICPR 2020
- Jun 2020: Gave an invited talk at AI4HC 2020
- May 2020: Defended my PhD Dissertation!!
- Apr 2020: Got selected to attend MLSS 2020, Tuebingen (13% acceptance)
- Apr 2020: Full paper accepted at CBMS 2020
- Apr 2020: Extension of our ICMLA 2019 paper accepted as a chapter for Deep Learning Applications, Volume 2 book
- Feb 2020: Presented our work on self-supervised semi-supervised multitasking at AAAI 2020, New York
- Jan 2020: Participated in the Deep Learning and Medical Applications Workshop at IPAM, UCLA
- Jan 2020: Attended the PhD Summit at Google LA
Old Old News
- Dec 2019: Presented our deep generative modeling paper at ICMLA 2019, Boca Raton, FL
- Dec 2019: Presented our work on landmark detection and vertebrae segmentation at Med-NeurIPS 2019, Vancouver, BC
- Dec 2019: Got awarded a scholarship for travel expenses to AAAI 2020
- Oct 2019: Paper accepted to AAAI 2020 Student Abstract and Poster Program
- Oct 2019: Presented our semi-supervised multitasking paper at MICCAI 2019 MLMI, Shenzhen, China
- Oct 2019: Two extended abstracts accepted to Med-NeurIPS 2019
- Sep 2019: Extension of our DLMIA 2018 paper accepted to the journal CMBBE: Imaging & Visualization
- Sep 2019: Paper accepted to ICMLA 2019 for oral (28% acceptance)!
- Aug 2019: Paper accepted to MLMI 2019
- Jun 2019: Joined Tencent America-Medical AI Lab, Palo Alto (Research Internship)
- Mar 2019: Advanced to PhD candidacy!
- Mar 2019: Got accepted with financial support to the DLRL Summer School 2019, Alberta
- Feb 2019: Received LabEx PRIMES fellowship to participate in DeepImaging 2019, Lyon
- Sep 2018: Our paper on lung lobe segmentation won the NVIDIA Best Paper Award at DLMIA 2018
- Sep 2018: Presented our pulmonary lobe segmentation paper at MICCAI 2018 DLMIA, Granada, Spain
- Aug 2018: Attended the Machine Learning Summer School (MLSS) 2018, Madrid
- Jul 2018: Paper accepted to MICCAI DLMIA 2018
- Jun 2018: Joined Philips Research North America, Cambridge (Research Internship)
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Research
My research is primarily centered around artificial intelligence, computer vision, and medical imaging.
I am particularly interested in developing advanced AI-powered medical imaging tools for clinical applications.
Recently, I focus on semi-supervised learning with multi-task learning, self-supervised learning, and generative modeling.
I am also interested in visual representation learning and domain generalization for medical image analysis tasks.
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Theses
From Fully-Supervised, Single-Task to Scarcely-Supervised, Multi-Task Deep Learning for Medical Image Analysis
PhD Thesis 2020 at the University of California, Los Angeles, California, USA
[Abstract]
[Oral]
[BibTex]
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Estimation of Breast Anatomical Descriptors From Mastectomy CT Images
MS Thesis 2016 at Delaware State University, Dover, Delaware, USA
[Abstract]
[Oral]
[BibTex]
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Automatic Extraction of Road Networks From High Resolution Satellite Images
BS Thesis 2012 at the Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
[Abstract]
[Oral]
[BibTex]
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Selected Publications
SSIQA: Multi-Task Learning for Non-Reference CT Image Quality Assessment With Self-Supervised Noise Level Prediction
Abdullah-Al-Zubaer Imran,
Debashish Pal,
Bhavik Patel,
Adam Wang
IEEE International Symposium on Biomedical Imaging (ISBI) 2021
[Paper]
[Oral]
[BibTex]
A self-supervised regularizer predicting noise index, jointly optimized for reference-free CT image quality assessment.
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MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images
Ayaan Haque,
Abdullah-Al-Zubaer Imran,
Adam Wang,
Demetri Terzopoulos
IEEE International Symposium on Biomedical Imaging (ISBI) 2021
[Preprint]
[Poster]
[BibTex]
[Code]
A novel saliency birdge module adapted semi-supervised method, exploiting consistency augmentation and multi-source data, for jointly learning diagnostic classification and anatomical structure segmentation.
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Multi-Adversarial Variational Autoencoder Nets for Simultaneous Image Generation and Classification
Abdullah-Al-Zubaer Imran,
Demetri Terzopoulos
Deep Learning Applications, Volume 2, 2021
[Chapter]
[BibTex]
Joint learning of image generation and semi-supervised classification using a novel deep generative model with multiple discriminators.
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Partly Supervised Multi-task Learning
Abdullah-Al-Zubaer Imran,
Chao Hunag,
Hui Tang,
Wei Fan,
Yuan Xiao,
Dingjun Hao,
Zhen Qian,
Demetri Terzopoulos
International Conference on Machine Learning and Applications (ICMLA) 2020
[Paper]
[BibTex]
[Oral]
Self-supervised regularization for jointly learning medical image classification and segmentation in limited labeled data settings.
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Progressive Adversarial Semantic Segmentation
Abdullah-Al-Zubaer Imran,
Demetri Terzopoulos
International Conference on Pattern Recognition (ICPR) 2020
[Preprint]
[BibTex]
[Oral]
[Poster]
A domain generalization approach without requiring domain specific data for generalized and improved medical image segmentation.
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Fully-Automated Analysis of Scoliosis from Spinal X-Ray Images
Abdullah-Al-Zubaer Imran,
Chao Hunag,
Hui Tang,
Wei Fan,
Kenneth MC Cheung,
Michael To,
Zhen Qian,
Demetri Terzopoulos
IEEE 33rd International Symposium on Computer Based Medical Systems (CBMS) 2020
[Paper]
[BibTex]
[Oral]
[Video]
A fully-automated pipeline based on segmentation of scoliotic vertebrae to measure and classify severity of scoliosis from anterior-posterior spine X-rays.
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Self-supervised Semi-Supervised Multi-Context Learning for the Combined Classification and Segmentation of Medical Images
Abdullah-Al-Zubaer Imran,
Chao Hunag,
Hui Tang,
Wei Fan,
Yuan Xiao,
Dingjun Hao,
Zhen Qian,
Demetri Terzopoulos
AAAI Conference on Artificial Intelligence 2020
[Paper]
[BibTex]
[Poster]
A novel semi-supervised multiple-task model leveraging self-supervision and adversarial training, applied to classification and segmentation of medical images.
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Bipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images
Abdullah-Al-Zubaer Imran,
Chao Hunag,
Hui Tang,
Wei Fan,
Kenneth MC Cheung,
Michael To,
Zhen Qian,
Demetri Terzopoulos
Medical Imaging Meets NeurIPS (Med-NeruIPS) 2019
[Paper]
[BibTex]
[Poster]
To guide a CNN in the learning of spinal shape while detecting landmarks in X-ray images, a novel loss based on a bipartite distance (BPD) measure is proposed which consistently improves landmark detection performance.
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Fast and Automatic Segmentation of Pulmonary Lobes From Chest CT Using a Progressive Dense V-Network
Abdullah-Al-Zubaer Imran,
Ali Hatamizadeh,
Shilpa P. Ananth,
Xiaowei Ding,
Nima Tajbakhsh,
Demetri Terzopoulos
Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualization 2019
[Paper]
[BibTex]
Reliable, fast, and fully automated lung lobe segmentation based on a Progressive Dense V-Network (PDV-Net). This method can segment lung lobes in one forward pass of the network, with an average runtime of 2 seconds using a single Nvidia Titan XP GPU.
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Semi-Supervised Multi-Task Learning With Chest X-Ray Images
Abdullah-Al-Zubaer Imran,
Demetri Terzopoulos
MICCAI Machine Learning in Medical Imaging (MLMI) 2019
[Paper]
[BibTex]
[Poster]
[Code]
A novel multi-task learning model for jointly learning a classifier and a segmentor, from chest X-ray images, through semi-supervised learning. In addition, a new loss function that combines absolute KL divergence with Tversky loss (KLTV) to yield faster convergence and better generarlization of the such models.
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Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network
Abdullah-Al-Zubaer Imran,
Ali Hatamizadeh,
Shilpa P. Ananth,
Xiaowei Ding,
Nima Tajbakhsh,
Demetri Terzopoulos
MICCAI Deep Learning in Medical Image Analysis (DLMIA) 2018
[Paper]
[BibTex]
[Oral]
Nvidia Best Paper Award
First and fast such kind of model which achieved a Dice score of 0.939 on LIDC and 0.950 on LTRC datasets, significantly outperforming a 2D U-Net model and a 3D Dense V-Net.
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Reviewing
- IEEE Access
- Patterns - Cell Press
- Knowledge-Based Systems
- Deep Learning for Computer Vision (DLCV)
- IEEE Transactions on Medical Imaging (TMI)
- IEEE International Symposium on Biomedical Imaging (ISBI)
- Medical Image Computing & Computed Assisted Intervention (MICCAI)
- International Conference on Advances in Electronics Engineering (ICAEE)
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Teaching
Teaching Assistant of CS, UCLA Samueli School of Engineering
Spring 2020
Winter 2020
Spring 2019
Winter 2019
Fall 2018
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Lecturer of ECE, North South University (NSU)
Summer 2017
Spring 2017
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Lecturer of CSE, Ahsanullah University of Science & Technology (AUST)
Summer 2014
Fall 2013
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Lecturer of CSE, Northern University Bangladesh (NUB)
Fall 2012
Spring 2013
Summer 2013
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Conferences, Workshops, Etc.
- ICPR 2020
- ICMLA 2020
- NeurIPS 2020
- RSNA 2020
- MICCAI 2020
- MLSP 2020
- CT 2020
- CBMS 2020
- AAPM 2020
- MLSS 2020
- AI4HC 2020
- AAAI 2020, New York, NY, USA
- DLMA 2020, IPAM, UCLA
- PhD Summit 2020, Google LA
- ICMLA 2019, Boca Raton, FL, USA
- NeurIPS 2019, Vancouver, BC, Canada
- MICCAI 2019, Shenzhen, China
- DeepImaging 2019, Lyon, France
- MICCAI 2018, Granada, Spain
- MLSS 2018, Madrid, Spain
- NeurIPS 2017, Long Beach, CA, USA
- SPIE MI 2016, San Diego, CA, USA
- DE IDeAs 2016, Newark, DE, USA
- SPMB 2015, Philadelphia, PA, USA
- TELSIKS 2015, Nis, Serbia
- DE Neuroscience Symposium 2014, Newark, DE, USA
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