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Label-free cell counting and viability prediction with brightfield imaging and deep learning

Paper recorded by Signals 4 on 2026-10-07 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-07 on arXiv · recorded by Signals 4 on 2026-10-08

Category: cs.CV · 计算机视觉 · first seen 2026-10-08

Abstract

Cell viability assessment is a core requirement in cell culture systems, with critical applications in biopharmaceutical manufacturing and drug development. Conventionally, it is measured by adding membrane-impermeable dyes to a sample (a process called staining), which allows compromised cell membranes to be distinguished from intact ones. However, staining has several limitations: (a) chemical a

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#323 most recent of 756 cs.CV papers we have recorded · ↑ newer: Agentic RSR: Real-to-Sim-to-Real through Scene Reconstruction and Exec · ↓ older: MORCA: Offline-to-Online Reinforcement Learning for Adaptive Cache Reu
Cite this page: Label-free cell counting and viability prediction with brightfield imaging and deep learning: the #323 most recent of 756 cs.CV papers we have recorded (as of 2026-10-07). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/label-free-cell-counting-and-viability-prediction-with-brightfield-imaging-and-d.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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