Medical Image Segmentation
Multi-task Deep Learning for Automated Medical Image Analysis
Overview
This project developed advanced deep learning pipelines for cell segmentation in label-free optical microscopy images, achieving state-of-the-art accuracy in medical imaging applications.
The work addresses critical challenges in medical image analysis, including handling imbalanced datasets, achieving high dice scores for segmentation tasks, and developing robust pipelines that can generalize across different imaging conditions and cell types.
Key Features
Multi-task Learning
Simultaneous optimization of multiple segmentation objectives for improved accuracy.
Imbalanced Data Handling
Advanced techniques to handle class imbalance common in medical imaging datasets.
Label-Free Imaging
Works with phase-contrast microscopy without requiring fluorescent labels.
High Accuracy
Achieved 95%+ dice score on benchmark medical imaging datasets.
Technologies Used
Related Publications
Turkish Journal of Electrical Engineering and Computer Sciences (2021)
Improved cell segmentation using deep learning in label-free optical microscopy images.
View PaperIEEE SIU Conference 2020
Deep Learning based Segmentation Pipeline for Label-Free Phase-Contrast Microscopy Images.
View PaperTIPTEKNO 2019
Cell Segmentation of 2D Phase-Contrast Microscopy Images with Deep Learning Method.
View PaperInterested in Medical AI Research?
I'm open to collaborations on medical imaging and computer vision projects.
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