Papers for

pathology lab technicians

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Efficient nuclei segmentation model speeds up digital pathology analysis

From UNI2-h to ConvNeXt-T: Lightweight Nuclei Instance Segmentation via Knowledge Distillation

Abstract: Nuclei instance segmentation is a core task in digital pathology, yet high-accuracy models rely on large vision transformer (ViT) encoders whose inference speed cannot meet real-time clinical demands. We propose a lightweight scheme that distills the UNI2-h pathology foundation model into a ConvNeXt-Tiny student (Ours-T, 34.7M parameters, 1/20 of the teacher) via output-level knowledge distillation. Ours-T achieves an mPQ of 0.519 on PanNuke (98.8% of the teacher), a zero-shot bPQ of 0.668 on MoNuSeg, and an inference speed of 634.3 img/s, requiring only 0.045 s for full-resolution 1024^2 analysis (21.8x speedup). Experiments further show that multi-scale gated convolution (MALA) yields no gain under ViT encoders, and output-level distillation alone suffices for efficient knowledge transfer.

Mon 28 SeptComputer Vision and Pattern Recognition

The gist

High-accuracy models that identify and separate nuclei in pathology images usually run slowly, making them hard to use in real-time medical settings. The authors created a much smaller, faster model by teaching it to mimic a larger, complex one. This small model works almost as well but runs over 20 times faster, analyzing images quickly enough for clinical use. The study also found that more complex techniques aren't needed for this efficient knowledge transfer.

Open → 2609.35203v1