  The semantic segmentation task in pathology plays an indispensable role in
assisting physicians in determining the condition of tissue lesions. Foundation
models, such as the SAM (Segment Anything Model) and SAM2, exhibit exceptional
performance in instance segmentation within everyday natural scenes. SAM-PATH
has also achieved impressive results in semantic segmentation within the field
of pathology. However, in computational pathology, the models mentioned above
still have the following limitations. The pre-trained encoder models suffer
from a scarcity of pathology image data; SAM and SAM2 are not suitable for
semantic segmentation. In this paper, we have designed a trainable
Kolmogorov-Arnold Networks(KAN) classification module within the SAM2 workflow,
and we have introduced the largest pretrained vision encoder for histopathology
(UNI) to date. Our proposed framework, SAM2-PATH, augments SAM2's capability to
perform semantic segmentation in digital pathology autonomously, eliminating
the need for human provided input prompts. The experimental results demonstrate
that, after fine-tuning the KAN classification module and decoder, Our dataset
has achieved competitive results on publicly available pathology data. The code
has been open-sourced and can be found at the following address:
https://github.com/simzhangbest/SAM2PATH.
