Abstract
Modern computer vision models have proven to be highly useful for medical imaging classification and segmentation tasks, but the scarcity of medical imaging data often limits the efficacy of models trained from scratch. Transfer learning has emerged as a pivotal solution to this, enabling the fine-tuning of high-performance models on small data. Mei et al. (2022) found that pre-training CNNs on a large dataset of radiologist-labeled images (RadImageNet) enhanced model performance on downstream tasks compared to ImageNet pretraining. The present work extends Mei et al. (2022) by conducting a comprehensive investigation to determine optimal CNN architectures for breast lesion malignancy detection and ACL tear detection, as well as performing statistical analysis to compare the effect of RadImageNet and ImageNet pre-training on downstream model performance. Our findings suggest that 1-dimensional convolutional classifiers with skip connections, ResNet50 pre-trained backbones, and partial backbone unfreezing yields optimal downstream medical classification performance. Our best models achieve AUCs of 0.9969 for ACL tear detection and 0.9641 for breast nodule malignancy detection, competitive with the results reported by Mei et al. (2022) and surpassing other previous works. We do not find evidence confirming RadImageNet pre-training to provide superior downstream performance for ACL tear and breast lesion classification tasks.
Abstract (translated)
现代计算机视觉模型已被证明在医学影像分类和分割任务中非常有用，但医疗影像数据的稀缺性往往限制了从头开始训练的模型的效果。迁移学习已经成为解决这一问题的关键方案，它使得在小规模数据集上对高性能模型进行微调成为可能。Mei等人（2022年）发现，在一个大规模的放射科医生标注图像的数据集（RadImageNet）上预训练卷积神经网络（CNN），相比使用ImageNet预训练，可以提高下游任务的表现。 本研究在Mei等人的基础上进行了扩展，通过全面调查来确定最优的CNN架构用于乳腺病变恶性检测和前交叉韧带撕裂检测，并进行统计分析以比较RadImageNet与ImageNet预训练对下游模型性能的影响。我们的发现表明，具备跳跃连接的一维卷积分类器、使用ResNet50预训练骨干网络以及部分解冻的骨干网络能够为下游医学分类任务提供最佳性能。 我们最好的模型在前交叉韧带撕裂检测中达到了AUC值为0.9969，在乳腺结节恶性检测中的AUC值为0.9641，与Mei等人（2022年）报告的结果相当，并且超越了其他先前的工作。然而，我们没有找到证据支持RadImageNet预训练在前交叉韧带撕裂和乳腺病变分类任务中提供比ImageNet预训练更好的下游性能。
URL
https://arxiv.org/abs/2508.17567
