🧬 Computational Biology¶

🎞️ ECCV2026 · 2 paper notes

CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning: Without touching the generative architecture, CellFluxRL treats seven automatically computable biological evaluators (MoA consistency, nucleus-in-cytoplasm containment, nuclear roundness, and size/count statistics of nuclei and cytoplasm) as rewards, and post-trains the flow-matching virtual cell model CellFlux with RL to push "visually realistic" cell images toward "biologically meaningful" ones; all seven rewards improve over the baselines, and best-of-N test-time scaling amplifies the gains further.
FPicker: Topology-Guided Evolution for Filament Tracing in Low-SNR Microscopy: Tsinghua University researchers present FPicker, which constructs a coarse topological skeletal prior via a box-free center-endpoint representation and iteratively deforms it through an open-boundary graph convolutional snake module with bridge curriculum training, eliminating ghost center drift and tip shrinking in low-SNR Cryo-EM filament tracing (SNR = -20 dB).