UCell：重新思考生物医学视觉模型的通用性和扩展性
UCell: rethinking generalizability and scaling of bio-medical vision models
AI总结：
本文提出UCell模型，通过递归结构提升小模型性能，证明其在单细胞分割任务中可与大模型媲美，且无需大规模预训练数据，具备良好的泛化能力。
摘要：
现代深度学习领域以规模为中心，较大的模型在相同架构下通常表现优于较小的模型。然而，在许多生物医学研究子领域中，模型的扩展受到可用训练数据量和生成和验证高质量额外数据的高成本的限制。尽管存在实际障碍，大多数持续研究仍集中在构建更大的基础模型上，而改进小型模型能力的替代方法却研究不足。本文实验性地构建了具有10-3000万参数的小型模型，以执行单细胞分割任务。重要的设计选择是将递归结构纳入模型的前向计算图中，从而形成更参数高效的架构。我们发现，在多个基准测试中，针对单细胞分割任务，我们的小型模型UCell在性能上可与比其大10-20倍的模型相媲美，并在未见的跨领域数据上表现出相似的泛化能力。更重要的是，我们发现UCell可以仅使用显微镜成像数据从头开始训练，而无需依赖大规模自然图像的预训练，因此将模型构建与任何外部商业利益解耦。最后，我们通过在多样化的小型数据集上执行广泛的单次和少次微调实验，检验并确认了UCell的适应性。实现代码已发布在https://github.com/jiyuuchc/ucell。
英文摘要：
The modern deep learning field is a scale-centric one. Larger models have been shown to consistently perform better than smaller models of similar architecture. In many sub-domains of biomedical research, however, the model scaling is bottlenecked by the amount of available training data, and the high cost associated with generating and validating additional high quality data. Despite the practical hurdle, the majority of the ongoing research still focuses on building bigger foundation models, whereas the alternative of improving the ability of small models has been under-explored. Here we experiment with building models with 10-30M parameters, tiny by modern standards, to perform the single-cell segmentation task. An important design choice is the incorporation of a recursive structure into the model's forward computation graph, leading to a more parameter-efficient architecture. We found that for the single-cell segmentation, on multiple benchmarks, our small model, UCell, matches the performance of models 10-20 times its size, and with a similar generalizability to unseen out-of-domain data. More importantly, we found that ucell can be trained from scratch using only a set of microscopy imaging data, without relying on massive pretraining on natural images, and therefore decouples the model building from any external commercial interests. Finally, we examined and confirmed the adaptability of ucell by performing a wide range of one-shot and few-shot fine tuning experiments on a diverse set of small datasets. Implementation is available at https://github.com/jiyuuchc/ucell
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
