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Portfolio item number 2
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publications
Demo Entry
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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Rise of Fluid Computing: A Collective Effort Of Mist, Fog and Cloud
Published in International Journal of Computer Science and Engineering, 2019
This paper is integrating Fog and Cloud computing for edge devices and forming a hybrid computing architecture called Fluid computing.
Complex-Phase Steel Microstructure Segmentation Using UNet: Analysis across Different Magnifications and Steel Types
Published in Materials, MDPI, 2023
This paper is about complex phase microstruture segmentation using UNet where we train the model on one type of steel and inference on other types.
Refining UNet3+ for Segmentation of Microstructures in Steel Images
Published in Proceedings of UCWIT, 2023
In this paper, we enhance the structural representation of steel images by modifying the UNet3+ design, including data processing, architectural modules, and a tailored loss function.
Phase Fraction Calculation in Multiphase Steel Microstructures Through Semantic Segmentation
Published in International Conference on Applied System Innovation (ICASI), 2024
In this paper we propose using semantic segmentation to identify and calculate the phase fraction in steel microstructures. We assign each pixel in an image to accomplish both phase identification and segmentation which we perform using enhanced ELU-Net.
Nuclei Segmentation in Histopathological Images with Enhanced U-Net3+
Published in Medical Imaging with Deep Learning [MIDL], 2024
In this paper we propose a task-specific feature sensitive U-Net model, that sets a baseline standard in segmentation of nuclei in histopathological images. We also perform various ablation studies and test their impact on nuclei images.
SAM Guided Task-Specific Enhanced Nuclei Segmentation in Digital Pathology
Published in Medical Image Computing and Computer Assisted Intervention [MICCAI], 2024
In this paper we propose utilizing the foundation model to guide the task-specific supervised learning by dynamically combining their global and local latent representations, via our proposed X-Gated Fusion Block, which uses Gated squeeze and excitation block followed by Cross-attention to dynamically fuse latent representations.
Enhancing Nuclei Segmentation in Histology Images by Effectively Merging Task-Specific and Foundation Models
Published in Journal of Multimedia Information System, 2024
In this paper we propose a novel approach to bridge this gap by merging the strengths of task-specific and foundation models. We introduce a Gated Fusion Block that leverages the task-specific capabilities of U-Net-like models. This work is the extension of our work published in MICCAI 2024.
Out-of-Distribution Nuclei Segmentation in Histology Imaging via Liquid Neural Networks with Modern Hopfield Layer
Published in Medical Image Computing and Computer Assisted Intervention [MICCAI], 2025
We present a nuclei segmentation framework that integrates Liquid Neural Networks with a Modern Hopfield Layer to improve robustness under distribution shifts in histology imaging. By processing embeddings in reverse hierarchical order and stabilizing them through associative memory, our method enhances domain-invariant representations. Experiments on benchmark datasets show an average 16.35% OOD improvement over baselines.
research
Allergic Rhinitis
Published:
Allergic rhinitis diagnosis from nasal endoscopic images.
Alloy Steel Microstructure
Published:
Deep learning segmentation of alloy steel microstructures from SEM images under extreme data and texture ambiguity.
Cell Nuclei Segmentation
Published:
This is an on-going project and has closed access before publication.
Screen Panel Anomaly
Published:
Automated detection of subtle surface anomalies on industrial display panels using structured illumination and deep learning.
Early Fire Detection
Published:
Patch-based fire detection system combining classical vision and deep learning for fast, robust early-stage fire localization on edge devices.
OOD Segmentation
Published:
This is an on-going project and has closed access before publication.
Battery Particle Segmentation
Published:
Adapting foundation segmentation models to dense, non-describable particle imagery using semi-automatic labeling and parameter-efficient fine-tuning.
Temperature Super Resolution
Published:
Learning-based super resolution of thermal images to reconstruct high-resolution temperature fields from low-resolution sensor data.
Next-Gen Neural Networks
Published:
This is an on-going project and has closed access before publication.
ClimaX via Radar
Published:
Regional radar-based rainfall forecasting using the ClimaX transformer with multi-variable radar inputs.
Pipe Defect Detection
Published:
Problem
Internal inspection of rocket engine pipes requires reliable detection of surface defects under challenging conditions, including cylindrical geometry, specular reflections, uneven illumination, and subtle texture variations. The goal is to detect defect regions with high precision while minimizing false positives in defect-free pipes.
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.