Abdullah-Al-Zubaer Imran
Email: aimran [AT] Stanford [DOT] edu

I am a Postdoctoral Research Scholar in the Wang group in the Radiological Sciences Laboratory (RSL) at Stanford University.

I obtained my PhD in Computer Science from the University of California, Los Angeles (UCLA) in 2020 under the supervision of Distinguished Professor Demetri Terzopoulos. During my PhD, as a member of the UCLA Graphics and Vision Lab, I worked on developing effective deep learning techniques for medical image analysis.

Earlier, I received an MS in Computer Science degree from Delaware State University (DSU) in 2016 and a Bachelor in Computer Science and Engineering (CSE) degree from Rajshahi University of Engineering and Technology (RUET), Bangladesh in 2012.

Previously, I worked in the Medical Imaging and Simulation (MEDIS) Lab at DSU in collaboration with the Penn XPL Lab, jointly advised by Professor David Pokrajac and Professor Predrag Bakic. I have also spent time at Philips Corporate Research NA at Cambridge, MA and Tencent Medical AI Lab at Palo Alto, CA.

profile photo

I am looking for undergraduates or graduate students at UCLA/Stanford to work on projects that involve machine learning, computer vision, and medical imaging. Email me for details.

I am always happy to learn about interesting opportunities. Please email me with details.

Check out:                           

News

  • Jan 2021: Two papers accepted at ISBI 2021!
  • Dec 2020: Presented our Partly supervised multi-task learning paper at ICMLA 2020 [Slides]
  • Oct 2020: Gave a guest lecture on Effective deep learning from limited labeled medical image data 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 webinar
  • Sep 2020: Full paper accepted at ICMLA 2020
  • Jul 2020: Presented our scoliosis analysis paper at CBMS 2020
  • Jul 2020: Joined Stanford as a Postdoc
  • Jun 2020: Paper accepted at ICPR 2020
Old News
  • 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
  • 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

Old Old News
  • 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)
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.

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]
Estimation of Breast Anatomical Descriptors From Mastectomy CT Images
MS Thesis 2016 at Delaware State University, Dover, Delaware, USA
[Abstract]  [Oral]  [BibTex]
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]
Selected Publications

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]  [BibTex]

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.

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.

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.

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.

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.

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.

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.

Multi-Adversarial Variational Autoencoder Networks
Abdullah-Al-Zubaer Imran, Demetri Terzopoulos
18th IEEE International Conference on Machine Learning & Applications (ICMLA) 2019
[Paper]  [BibTex]  [Oral]

A novel deep generative model which incorporate an ensemble of discriminators in a combined VAE-GAN network, with simultaneous adversarial learning and variational inference.

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.

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.

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.

animated
Characterization of adipose compartments in mastectomy CT images
Abdullah-Al-Zubaer Imran, Predrag R. Bakic, David D. Pokrajac
SPIE Medical Imaging: Physics in Medical Imaging 2018
[Paper]  [BibTex]  [Poster]

Distribution and orientation of adipose compartments segmented from CT images of a mastectomy specimen were investigatged. Ellipsoidal fitting was applied to the segmented compartments, by matching the moments of inertia. Compartment size, shape, and orientation were characterized by estimating the volume, axis ratio, and Euler’s angles of fitted ellipsoids.

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)
Teaching

Teaching Assistant of CS, UCLA Samueli School of Engineering

Spring 2020 Winter 2020 Spring 2019 Winter 2019 Fall 2018
Lecturer of ECE, North South University (NSU)

Summer 2017 Spring 2017
Lecturer of CSE, Ahsanullah University of Science & Technology (AUST)

Summer 2014
    Fall 2013
      Lecturer of CSE, Northern University Bangladesh (NUB)

      Fall 2012
        Spring 2013
          Summer 2013
            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
            Some Useful Links


            Last update: Oct 2020