BCT4: 3D U-Net Bottlenecks and Minimal CV Score Improvement (0.4483 -> 0.4578)
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Biohub - Cell Tracking During Development Abstract Constructed and trained a 3D U-Net model in PyTorch for 3D cell centroid detection. Observed a slight CV score increase from 0.4483 to 0.4578, falling far short of top leaderboard scores(0.982). Analyzed key technical bottlenecks including extreme 3D class imbalance, spatial resolution loss, and tracking limits. Overview & Background In our local validation setup, simple intensity thresholding(blob_dog) struggled to separate dark, dim cells…
1Key Takeaways
- Biohub - Cell Tracking During Development Abstract Constructed and trained a 3D U-Net model in PyTorch for 3D cell centroid detection.
- Observed a slight CV score increase from 0.4483 to 0.4578, falling far short of top leaderboard scores(0.982).
- Analyzed key technical bottlenecks including extreme 3D class imbalance, spatial resolution loss, and tracking limits.
- Overview & Background In our local validation setup, simple intensity thresholding(blob_dog) struggled to separate dark, dim cells….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that biohub - Cell Tracking During Development Abstract Constructed and trained a 3D U-Net model in PyTorch for 3D cell centroid detection.
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