Performance comparison of federated learning algorithms for nucleus segmentation with INT8 quantized models.

Performance comparison of federated learning algorithms for nucleus segmentation with INT8 quantized models.

<p>Performance comparison of federated learning algorithms for nucleus segmentation with INT8 quantized models.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Anjir Ahmed Chowdhury (21696531) (author)
مؤلفون آخرون:	S M Hasan Mahmud (21696534) (author), Md Palash Uddin (13473853) (author), Seifedine Kadry (8713629) (author), Jung-Yeon Kim (410325) (author), Yunyoung Nam (2559640) (author)
منشور في:	2025
الموضوعات:	Cell Biology Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified segnet model segments scalable automated solutions remain challenging due reduce computational overhead protect data privacy matthews correlation coefficient hyperparameter optimization using hyperband method enhances experimental results show densenet121 model classifies 0 %, respectively world clinical deployment mpa ), 63 deep learning framework enhance nuclei evaluation full integer quantization mean pixel accuracy miou ), mean intersection framework follows framework employs overlapping nuclei nuclei segmentation nuclei regions weighted iou study proposes stage approach segmented instances manual segmentation maintaining accuracy histopathology images histology images fwiou ). efficient deployment edge devices combines segmentation color inconsistency cancer detection blurry boundaries
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