这篇论文把迁移学习用在腹腔镜多器官分割上，CEMD模型微调后Dice到62.4%，还比从零训练快不少，做医学影像分割的值得看看。
该研究将类特定解码器架构扩展到跨手术域知识迁移，使用直肠和胆囊切除术两个数据集验证。实验表明，器官特定解码器模型CEMD在跨域预训练后全微调，分割性能达到62.4%Dice，且收敛速度明显快于从零训练。但类别不平衡问题依然存在，迁移学习对少数解剖结构的改善有限。
Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation
Effective multi-organ segmentation in surgical data requires learning the intricate anatomical features and alleviating the challenge of class imbalance, which results from relatively lower proportions of small and limitedly exposed structures. Recent works on laparoscopic multi-organ segmentation focus on learning structure-specific features through class-specific decoder architectures and report favorable results. This work extends the decoder-focused architectures to investigate knowledge sharing in the cross-surgical domain. We utilize two datasets representing different surgical domains, rectal and cholecystectomy surgeries, to explore how surgical conceptual knowledge transfers under partially common anatomical representations. Additionally, we compare the feature adaptation for the encoder and decoder at different training stages to analyse the knowledge adaptation and retention in the network. Our results corroborate previous findings on decoder-specific architectures and demonstrate that the organ-specific decoder model (CEMD), fully fine-tuned after cross-domain pre-training, achieves the highest segmentation performance (62.4\% dice) while converging substantially faster than training from scratch. However, we also find that class imbalance in surgical data remains a persistent challenge that transfer learning does not fully resolve for underrepresented anatomical structures.