Standardization and Cross-Platform Integration of Machine Learning Pipelines in Biomedical Imaging Diagnostics

This article explains how to utilize machine learning for medical imaging on several platforms. This pipeline ensures that the feature distributions from different suppliers are in sync, standardizes the inputs, and creates predictions that take calibration into consideration. This method makes compact latent representations that function on any platform by using covariance-guided harmonization, centering/whitening, kerneleigenspace alignment, multi-objective optimization, similarity regularization, and feature-importance reduction. Using an indomain Site-A test set, the suggested model receives a decent calibration (ECE 0.014; NLL 0.198), Sens@90%Spec 0.884, AUPRC 0.936 [0.919-0.951], and AUROC 0.952 [0.936-0.967]. The outcome will depend on how well the suggested paradigm works. When considering a distribution shift (Site-A → B), the following values are kept: Sens @ 90% \operatorname{Spec}=0.851, ECE =0.019, AUROC =0.928[0.909-0.945], and AUPRC =0.905[0.886 0.923]. The performance and calibration are both better than the existing baselines, which are BioMedCLIP-ViT-B (+\mathbf{0.026} AUROC) and ConvNeXtV2-B (+0.037 AUROC). The findings indicate that the diagnostics are universally sensitive to operating points, can be replicated, and are dependable. These traits are particularly significant for real-world application.