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Iftekhar Ahmed
RA @ InteX
Working on computer vision
Sylhet, Bangladesh
01:50:17 AM
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March 11, 2026
Updates:
Mar 10, 2026
SubmittedSubmittedSubmittedPaper Submitted
Submitted our extended work on TFFM to CV4Clinical @ CVPR 2026.
Jan 14, 2026
Paper Published ✔
Our paper 'TFFM: Topology-Aware Feature Fusion Module via Latent Graph Reasoning for Retinal Vessel Segmentation' is published in the proceedings of P2P-CV @ WACV 2026 (Oral Presentation). More Info
Nov 10, 2025
Paper Accepted ✔
Our paper titled Multi-Strategy Optimization of U-Net Variants for Orthopantomogram Segmentation has been accepted at the 4th IEEE Conference on Biomedical Engineering, Computer and Information Technology for Health 2025 (IEEE BECITHCON 2025)
2025
OngoingOngoingOngoingNational Cybersecurity Authority (NCA) Cybersecurity Research & Innovation Pioneers Grant
Awarded the prestigious NCA Cybersecurity Research & Innovation Pioneers Grant by the National Cybersecurity Authority of the Kingdom of Saudi Arabia for the research proposal "Privacy-Preserving Federated Learning Platform for the Healthcare Domain".
Nov 1, 2025
SubmittedSubmittedSubmittedAdversarial Attack / Defense project [InteX funded]
Completed the InteX funded research project focusing on adversarial attack/defense. Submitted the manuscript on Scientific Reports
Featured Publications:
I. Ahmed, S. Absar, A. A. Sami, S. Sakib, D. Biswas and S. A. M. Mostafa, "TFFM: Topology-Aware Feature Fusion Module via Latent Graph Reasoning for Retinal Vessel Segmentation," in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) P2P-CV Workshop, pp. 359-368, Mar. 2026. | Link
I. Ahmed, B. Bushon Routh, M. S. Rahman Kohinoor, S. Sakib, M. Mahfuzur Rahman and F. Azzedin, "Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification," in IEEE Access Q1, vol. 12, pp. 181009-181024, 2024, doi: 10.1109/ACCESS.2024.3510224.
Events:
Worked with:
AI/ML
PythonOpenCVPyTorchNumPyPandasTensorFlow
WebDev
ReactTypeScriptNextTailwindNodeExpressNestZodReactQuery
Misc.
PostgreSQLMySQLMongoDBPrisma
Featured Blogs:
August 5, 2025
Pixel-by-Pixel Image Creation: Understanding PixelCNN
This blog post aims to make PixelCNN easy to understand for beginners. We’ll start with an example to show the problem PixelCNN is designed to solve, then break down its core idea of using masked convolutions to model images pixel by pixel. From there, we’ll walk through its architecture step-by-step, including Type A and Type B masks, and build a simple PixelCNN from scratch using PyTorch. We’ll also discuss its strengths, limitations, and how it connects to similar ideas in other fields. By the end of this blog, you’ll have a clear understanding of what PixelCNN is, how it works, and how to implement it yourself.
July 25, 2025
The Game of Deception: Generative Adversarial Network (GANs)
The blog post explores Generative Adversarial Networks (GANs), which consist of two neural networks, the Generator and the Discriminator, competing in a dynamic process to create realistic data. Unlike Variational Autoencoders (VAEs), GANs produce sharp images by having the Generator create fake data while the Discriminator evaluates its authenticity. This adversarial interaction drives both networks to improve continuously, resulting in highly convincing outputs. The post explains the architecture and training of GANs using an analogy of a counterfeiter and a detective and discusses challenges like mode collapse and training instability. It concludes with a practical example of building a GAN using PyTorch and the Fashion-MNIST dataset, showcasing the generation of new fashion items.
July 20, 2025
Neural Compression Techniques Building Autoencoders and VAEs
This blog post aims to demystify autoencoders for beginners. We'll start with an example to illustrate the problem autoencoders solve, then dive into their fundamental architecture, and finally, build a simple autoencoder from scratch using PyTorch. We'll also explore their limitations and introduce you to the concept of Variational Autoencoders (VAEs), which address some of these challenges and build it from scratch. By the end of this blog, you'll have a solid grasp of what autoencoders are, how they work, and where they fit in, and you'll build an autoencoder along with a variational autoencoder from scratch.