发表机构
Research Institute CODE, UniBw, Munich; Amsterdam UMC（慕尼黑联邦国防军大学CODE研究所; 阿姆斯特丹大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对紧凑型医学图像分类器需求，提出qZACH-ViT及RASO。通过在七个数据集上实验，qZACH-ViT提升分类性能，RASO增强稳定性，二者确立了可部署、可解释的模型及优化程序。
AI 中文摘要
紧凑型医学图像分类器需要效率和可解释的证据，但这些目标通常是分开处理的。我们引入了qZACH-ViT，它是零令牌（无CLS令牌）、无位置的ZACH-ViT主干的量化感知扩展，具有递归内在补丁级分类证据。我们还引入了递归归因稳定优化（RASO），它使分类和归因梯度的范数匹配，并去除与分类冲突的归因成分。我们在七个MedMNIST数据集上评估了四种受控条件，每个类别使用50张训练图像和十个固定种子，共完成280次运行。所有210个qZACH-ViT检查点都转换为可执行的混合精度ONNX INT8图，包含16个带INT32累加的有符号INT8 MatMulInteger投影。部署的混合精度INT8 qZACH-ViT与Adam相比，在所有七个数据集上提高了FP32 ZACH-ViT基线均值，在特定数据集的主要指标上平均配对增益为0.0313；带有RASO的qZACH-ViT平均增益为0.0368。在964,920次源到INT8测试比较中，预测一致性为99.9751%，主要指标的平均绝对变化为0.000133，最大为0.004386。在3600个匹配的内在映射中，平均余弦相似度为0.999955，平均秩相关为0.9944，平均前10%重叠为0.9692。ONNX工件比源检查点小70.0%，在单线程和四线程时分别提供1.41倍和2.39倍的端到端CPU加速。RASO在相同归因损失下比Adam显著降低了充分性误差并提高了输入噪声稳定性，但并非在每个预测或可解释人工智能（XAI）指标上都占主导。这些结果确立了qZACH-ViT作为可部署的紧凑型内在可解释模型，以及RASO作为有针对性的面向稳定性的优化程序。
英文摘要
Compact medical-image classifiers need efficiency and interpretable evidence, yet these goals are often addressed separately. We introduce qZACH-ViT, a quantization-aware extension of the zero-token (CLS-token-free), position-free ZACH-ViT backbone with recursive intrinsic patch-level class evidence. We also introduce Recursive Attribution-Stabilized Optimization (RASO), which norm-matches classification and attribution gradients and removes attribution components that conflict with classification. We evaluate four controlled conditions on seven MedMNIST datasets using 50 training images per class and ten fixed seeds, completing 280 runs. All 210 qZACH-ViT checkpoints are converted to executable mixed-precision ONNX INT8 graphs containing 16 signed INT8 MatMulInteger projections with INT32 accumulation. Deployed mixed-precision INT8 qZACH-ViT with Adam improves the FP32 ZACH-ViT baseline mean on all seven datasets, with a mean paired gain of 0.0313 in the dataset-specific primary metric; qZACH-ViT with RASO yields a mean gain of 0.0368. Across 964,920 source-to-INT8 test comparisons, prediction agreement is 99.9751\%, with a mean absolute primary-metric change of 0.000133 and a maximum of 0.004386. Across 3,600 matched intrinsic maps, mean cosine similarity is 0.999955, mean rank correlation is 0.9944, and mean top-10\% overlap is 0.9692. ONNX artifacts are 70.0\% smaller than source checkpoints and provide $1.41\times$ and $2.39\times$ end-to-end CPU speedups with one and four threads. RASO significantly reduces sufficiency error and improves input-noise stability over Adam with the same attribution loss, but does not dominate every predictive or explainable artificial intelligence (XAI) metric. These results establish qZACH-ViT as a deployable compact intrinsically explainable model and RASO as a targeted stability-oriented optimization procedure.
Comments31 pages, 8 figures, 10 tables. Includes executable mixed-precision ONNX INT8 deployment validation across 210 checkpoints
