CV

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Research Statement

Most deep learning-based methods require a considerable amount of labeled data, which is difficult to come by in the computer vision and medical field. I am interested in developing a unsupervised domain adaptation methods to learn from synthetic data to mitigate the labeling efforts.

Keywords: Deep learning, Unsupervised Domain Adaptation, Segmentation, Classification, Detection.

Education

• B.S. in Computer Science, Abdul Wali Khan University, 2010-2014
• M.S. in Computer Science and Engineering, Chonbuk National University, 2015-2017
• Ph.D in Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technololgy, 2018-2023

Work experience

• 2018-2023 🠚 Doctoral Research Student:
  • Medical Image and Signal Processing Lab (MISPL)
  • Duties included: Domain adaptation for semantic segmentation (Computer Vision/ Medical Field)
  • Supervisor: Prof. Sang Hyun Park
• 2017-2018 🠚 Post M.S Researcher:
  • Medical Image and Signal Processing Lab (MISPL)
  • Duties included: Surgical Tool Segmentation and Tracking
  • Supervisor: Prof. Sang Hyun Park
• 2015-2017 🠚 M.S Student Researcher:
  • Software Engineering Lab (SEL)
  • Duties included: Vehicle Detection and Classification
  • Supervisor: Prof. Hyo Jong Lee

Articles in Submission/Preparation

Video Domain Adaptation for Semantic Segmentation.

Ihsan Ullah et al. (2022). Video Domain Adaptation for Semantic Segmentation.(ESWA)</i>

Publications

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease.

Ihsan Ullah et al. (2022). Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease; Frontiers in Plant Science. 1(1).

Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation.

Ihsan Ullah et al. (2021). Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation; Applied Sciences. 1(1).

Real-Time Tracking of Guidewire Robot Tips Using Deep Convolutional Neural Networks on Successive Localized Frames.

Ihsan Ullah et al. (2019). Real-Time Tracking of Guidewire Robot Tips Using Deep Convolutional Neural Networks on Successive Localized Frames; IEEE Access. 1(1).

Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture.

Ihsan Ullah et al. (2019). Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture; Sensons. 1(1).

Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities.

Ihsan Ullah et al. (2021). Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities; Computers, Materials & Continua . 1(1).

Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learning.

Ihsan Ullah et al. (2022). Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learning; Diagnostics. 1(1).

A deep learning based dual encoder–decoder framework for anatomical structure segmentation in chest X-ray images

Ihsan Ullah et al. (2023). A deep learning based dual encoder–decoder framework for anatomical structure segmentation in chest X-ray images; Scientific Reports. 1(1).

Conferences

An Approach of Locating Korean Vehicle License Plate Based on Mathematical Morphology and Geometrical Features

Ihsan Ullah et al. (2016). An Approach of Locating Korean Vehicle License Plate Based on Mathematical Morphology and Geometrical Features; 2016 International Conference on Computational Science and Computational Intelligence (CSCI).. 1(1).

An Effective Algorithm for Shadow Removal from Moving Vehicles Based on Morphology

Ihsan Ullah et al. (2016). An Effective Algorithm for Shadow Removal from Moving Vehicles Based on Morphology; 2016 International Symposium on Information Technology Convergence.. 1(1).

Moving Object Detection Based on Background Subtraction

Ihsan Ullah et al. (2016). Moving Object Detection Based on Background Subtraction; 2016 Conference of KIISE, South Korea.. 1(1).

License Plate Detection Based on Rectangular Features and Multilevel Thresholding

Ihsan Ullah et al. (2016). License Plate Detection Based on Rectangular Features and Multilevel Thresholding; 2016 IPCV.. 1(1).

Moving Vehicle Detection and Information Extraction Based on Deep Neural Network

Ihsan Ullah et al. (2017). Moving Vehicle Detection and Information Extraction Based on Deep Neural Network; 2017 Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV); Athens, (2017).. 1(1).

Catheter Synthesis in X-Ray Fluoroscopy with Generative Adversarial Networks

Ihsan Ullah et al. (2019). Catheter Synthesis in X-Ray Fluoroscopy with Generative Adversarial Networks; MICCAI PRIME. 1(1).

Guidewire Tip Tracking using U-Net with Shape and Motion Constraints

Ihsan Ullah et al. (2019). Guidewire Tip Tracking using U-Net with Shape and Motion Constraints; 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). 1(1).

Seminar and Talks

Talk 2 on Relevant Topic in Your Field

February 01, 2014

Talk at London School of Testing, London, UK

Conference Proceeding talk 3 on Relevant Topic in Your Field

March 01, 2014

Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA

Presented a webinar on Catheter and Guidewire Segmentation via Adversarial Learning.

March 27, 2022

Talk at Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology., Pakistan

Self-Supervised Learning for Catheter Tracking in Fluoroscopic Sequences using GANs

November 25, 2022

Tutorial at Annual Conference of Korean Society of Medical Robotics, Busan,South Korea

Teaching

Teaching Assistant for Machine Learning (Graduate)

Skills