标题： ModAn-MulSupCon：医学影像的模态和解剖结构感知多标签监督对比预训练 显示英文标题

标题： ModAn-MulSupCon: Modality-and Anatomy-Aware Multi-Label Supervised Contrastive Pretraining for Medical Imaging

摘要： 背景和目标：专家标注限制了医学影像中的大规模监督预训练，而普遍存在的元数据（模态、解剖区域）仍未被充分利用。 我们引入了ModAn-MulSupCon，这是一种模态和解剖感知的多标签监督对比预训练方法，利用此类元数据来学习可迁移的表示。 方法：每个图像的模态和解剖区域被编码为一个多热向量。 使用Jaccard加权多标签监督对比损失，在RadImageNet的一个小子集（miniRIN，16,222张图像）上对ResNet-18编码器进行预训练，然后通过微调和线性探测在三个二分类任务上进行评估——ACL撕裂（膝关节MRI）、病灶恶性（乳腺超声）和结节恶性（甲状腺超声）。 结果：经过微调，ModAn-MulSupCon在MRNet-ACL（0.964）和甲状腺（0.763）上达到了最佳AUC，超过了所有基线（$p<0.05$），在乳腺（0.926）上排名第二，仅次于SimCLR（0.940；不显著）。 当编码器冻结时，SimCLR/ImageNet表现更优，这表明ModAn-MulSupCon的表示最受益于任务适应，而不是线性可分性。 结论：将易于获得的模态/解剖元数据编码为多标签目标提供了一种实用且可扩展的预训练信号，在微调可行时可以提高下游准确性。 ModAn-MulSupCon是标签稀缺临床环境中的强大初始化方法，而SimCLR/ImageNet在冻结编码器部署中仍更受青睐。 显示英文摘要

摘要： Background and objective: Expert annotations limit large-scale supervised pretraining in medical imaging, while ubiquitous metadata (modality, anatomical region) remain underused. We introduce ModAn-MulSupCon, a modality- and anatomy-aware multi-label supervised contrastive pretraining method that leverages such metadata to learn transferable representations. Method: Each image's modality and anatomy are encoded as a multi-hot vector. A ResNet-18 encoder is pretrained on a mini subset of RadImageNet (miniRIN, 16,222 images) with a Jaccard-weighted multi-label supervised contrastive loss, and then evaluated by fine-tuning and linear probing on three binary classification tasks--ACL tear (knee MRI), lesion malignancy (breast ultrasound), and nodule malignancy (thyroid ultrasound). Result: With fine-tuning, ModAn-MulSupCon achieved the best AUC on MRNet-ACL (0.964) and Thyroid (0.763), surpassing all baselines ($p<0.05$), and ranked second on Breast (0.926) behind SimCLR (0.940; not significant). With the encoder frozen, SimCLR/ImageNet were superior, indicating that ModAn-MulSupCon representations benefit most from task adaptation rather than linear separability. Conclusion: Encoding readily available modality/anatomy metadata as multi-label targets provides a practical, scalable pretraining signal that improves downstream accuracy when fine-tuning is feasible. ModAn-MulSupCon is a strong initialization for label-scarce clinical settings, whereas SimCLR/ImageNet remain preferable for frozen-encoder deployments.