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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.
research
Talk 1 on Relevant Topic in Your Field
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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