@inproceedings{d37df269263e4d7aa31647a2d6a24139,
title = "Adapting SAM to Histopathology Images for Tumor Bud Segmentation in Colorectal Cancer",
abstract = "Colorectal cancer (CRC) is the third most common cancer in the United States. Tumor Budding (TB) detection and quantification are crucial yet labor-intensive steps in determining the CRC stage through the analysis of histopathology images. To help with this process, we adapt the Segment Anything Model (SAM) on the CRC histopathology images to segment TBs using SAM-Adapter. In this approach, we automatically take task-specific prompts from CRC images and train the SAM model in a parameter-efficient way. We compare the predictions of our model with the predictions from a trained-from-scratch model using the annotations from a pathologist. As a result, our model achieves an intersection over union (IoU) of 0.65 and an instance-level Dice score of 0.75, which are promising in matching the pathologist{\textquoteright}s TB annotation. We believe our study offers a novel solution to identify TBs on H&E-stained histopathology images. Our study also demonstrates the value of adapting the foundation model for pathology image segmentation tasks.",
keywords = "Colorectal cancer, attention mechanism, cross-attention network, deep learning, histopathology images, multiple instance learning, tumor budding, weakly supervised learning",
author = "Ziyu Su and Wei Chen and Sony Annem and Usama Sajjad and Mostafa Rezapour and Frankel, {Wendy L.} and Gurcan, {Metin N.} and Niazi, {M. Khalid Khan}",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Medical Imaging 2024: Digital and Computational Pathology ; Conference date: 19-02-2024 Through 21-02-2024",
year = "2024",
doi = "10.1117/12.3006517",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Tomaszewski, {John E.} and Ward, {Aaron D.}",
booktitle = "Medical Imaging 2024",
}