COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation
Journal:
arXiv
Published Date:
Mar 14, 2025
Abstract
Cell instance segmentation (CIS) is crucial for identifying individual cell
morphologies in histopathological images, providing valuable insights for
biological and medical research. While unsupervised CIS (UCIS) models aim to
reduce the heavy reliance on labor-intensive image annotations, they fail to
accurately capture cell boundaries, causing missed detections and poor
performance. Recognizing the absence of error-free instances as a key
limitation, we present COIN (COnfidence score-guided INstance distillation), a
novel annotation-free framework with three key steps: (1) Increasing the
sensitivity for the presence of error-free instances via unsupervised semantic
segmentation with optimal transport, leveraging its ability to discriminate
spatially minor instances, (2) Instance-level confidence scoring to measure the
consistency between model prediction and refined mask and identify highly
confident instances, offering an alternative to ground truth annotations, and
(3) Progressive expansion of confidence with recursive self-distillation.
Extensive experiments across six datasets show COIN outperforming existing UCIS
methods, even surpassing semi- and weakly-supervised approaches across all
metrics on the MoNuSeg and TNBC datasets. The code is available at
https://github.com/shjo-april/COIN.
