bibtext: @InProceedings{ Hua_DESSAM_MICCAI2024,
                   author = { Huang, Lina and Liang, Yixiong and Liu, Jianfeng },
                   title = { { DES-SAM: Distillation-Enhanced Semantic SAM for Cervical Nuclear Segmentation with Box Annotation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: Nuclei segmentation in cervical cell images is a crucial technique for the automatic diagnosis of cervical cell pathology. The current state-of-the-art (SOTA) nuclei segmentation methods often require significant time and resources to provide pixel-level annotations for training. To reduce the labor-intensive annotation costs, we propose DES-SAM, a box-supervised cervical nucleus segmentation network with strong generalization ability based on self-distillation prompting. We utilize Segment Anything Model (SAM) to generate high-quality pseudo-labels by integrating a lightweight detector. The main challenges lie in the poor generalization ability brought by small-scale training datasets and the large-scale training parameters of traditional knowledge distillation frameworks. To address these challenges, we propose leveraging the strong feature extraction ability of SAM and a self-distillation prompting strategy to maximize the performance of the downstream nuclear semantic segmentation task without compromising SAM’s generalization. Additionally, we propose an Edge-aware Enhanced Loss to improve the segmentation capability of DES-SAM. Various comparative and generalization experiments on public cervical cell nuclei datasets demonstrate the effectiveness of the proposed method.
title: DES-SAM: Distillation-Enhanced Semantic SAM for Cervical Nuclear Segmentation with Box Annotation
authors: Huang, Lina Liang, Yixiong Liu, Jianfeng