bibtext: @InProceedings{ Swa_SAM_MICCAI2024,
                   author = { Swain, Bishal R. and Cheoi, Kyung J. and Ko, Jaepil },
                   title = { { SAM Guided Task-Specific Enhanced Nuclei Segmentation in Digital Pathology } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: Cell nuclei segmentation is crucial in digital pathology for various diagnoses and treatments which are prominently performed using semantic segmentation that focus on scalable receptive field and multi-scale information. In such segmentation tasks, U-Net based task-specific encoders excel in capturing fine-grained information but fall short integrating high-level global context. Conversely, foundation models inherently grasp coarse-level features but are not as proficient as task-specific models to provide fine-grained details. To this end, we propose utilizing the foundation model to guide the task-specific supervised learning by dynamically combining their global and local latent representations, via our proposed X-Gated Fusion Block, which uses Gated squeeze and excitation block followed by Cross-attention to dynamically fuse latent representations. Through our experiments across datasets and visualization analysis, we demonstrate that the integration of task-specific knowledge with general insights from foundational models can drastically increase performance, even outperforming domain-specific semantic segmentation models to achieve state-of-the-art results by increasing the Dice score and mIoU by approximately 12% and 17.22% on CryoNuSeg, 15.55% and 16.77% on NuInsSeg, and 9% on both metrics for the CoNIC dataset. Our code will be released at https://cvpr-kit.github.io/SAM-Guided-Enhanced-Nuclei-Segmentation/.
title: SAM Guided Task-Specific Enhanced Nuclei Segmentation in Digital Pathology
authors: Swain, Bishal R. Cheoi, Kyung J. Ko, Jaepil