Automated Cellularity Assessment in Bone Marrow Using Deep Learning-Based Segmentation
A submitted medical-AI manuscript on bone-marrow cellularity assessment using segmentation.
A bone-marrow cellularity assessment manuscript using deep learning-based segmentation for clinical image-analysis support.
problem
Bone-marrow cellularity assessment requires robust segmentation and clinically meaningful aggregation rather than a single generic image-classification output.
key idea
Use a segmentation-first pipeline to turn marrow imagery into cellularity estimates that can be evaluated and interpreted.
my role
Technical contributor for data/evaluation workflow and manuscript support.
methods
- • Deep learning segmentation
- • Bone-marrow image analysis
- • Cellularity estimation
- • Clinical evaluation framing
evidence / results
- • Submitted manuscript tracked in the Overleaf project index
- • Related to the MekaNet/cellularity pathology axis
why this belongs in the portfolio
- • Adds a second pathology task beyond detection/classification
- • Shows the recurring pattern of domain data → structured measurement → evaluation
authors
Jae-Hyun Baek et al.
venue / status
Submitted medical AI manuscript
Submitted/draft manuscript; public details remain high-level pending final venue status.
tags