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.

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:                           

• 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

• 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

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.

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.

Joint learning of image generation and semi-supervised classification using a novel deep generative model with multiple discriminators.

Self-supervised regularization for jointly learning medical image classification and segmentation in limited labeled data settings.

A domain generalization approach without requiring domain specific data for generalized and improved medical image segmentation.

A fully-automated pipeline based on segmentation of scoliotic vertebrae to measure and classify severity of scoliosis from anterior-posterior spine X-rays.

A novel semi-supervised multiple-task model leveraging self-supervision and adversarial training, applied to classification and segmentation of medical images.

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.

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

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.

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.

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.

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.

• 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)

• CS168: Computational Methods for Medical Imaging
  [Course Site]   [TA Discussion (Section 1D)]

• CS31: C++ Programming  
  [Course Site]   [TA Discussion (Section 1J)]

• CS31: C++ Programming  
  [Course Site]   [TA Discussion (Section 1G)]

• CS31: C++ Programming  
  [Course Site]   [TA Discussion (Section 1C)]

• CS31: C++ Programming  
  [Course Site]   [TA Discussion (Section 2A)]

• CSE 115: Programming Language I  
  [Course Site]
• CSE 173: Discrete Mathematics  
  [Course Site]
• CSE 273: Theory of Computation  
  [Course Site]
• CSE 410: Management of Information Technology  
  [Course Site]

• CSE 115: Programming Language I  
  [Course Site]
• CSE 173: Discrete Mathematics  
  [Course Site]
• CSE 226: Data Structures & Algorithms  
  [Course Site]
• CSE 273: Theory of Computation  
  [Course Site]

• 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

• MICCAI Daily 2019 official magazine by RSIP Vision

• Key Takeaways from MICCAI 2018 by Nvidia

• Advice for prospective graduate students by Prof. David Evans

• Advice on Giving a Talk by Prof. Jonathan Shewchuk

Last update: Oct 2020