Assessing the performance of fully supervised and weakly supervised learning in breast cancer histopathology

Fully supervised learning (FSL) and weakly supervised learning based on multiple instance learning (WSLMIL) have become two mainstream paradigms for performing computer-aided pathological diagnosis (CAPD). It is well known that the high-intensity annotation burden of FSL and the performance degradation due to poor training constraints of WSLMIL are stumbling blocks for clinical translation. Even more unfortunate is the lack of comprehensive experimental analysis to help researchers make content-specific trade-offs between FSL and WSLMIL. In this work, we systematically compare the performances of FSL and WSLMIL on lymph node metastasis in breast cancer using a publicly available dataset. By analyzing the results of 16 backbone networks in the FSL paradigm, we find that emerging networks based on transformer (PVTv2-B2) and multi-layer perceptron (CycleMLP-B3) are more advantageous for performing patch-level classification task than convolution-based structure (ResNet50); combining their output with morphological feature extraction can be better used to universally perform slide-level classification task. However, the slight improvement brought by the evolution of the backbone network may be overshadowed by the aggregation operation in 6 WSLMIL algorithms, whereas relying on the in-domain backbone network can achieve a stable and excellent prediction performance in both quantitative analysis and interpretability comparisons. All the experimental results ultimately illustrate that the combination of in-domain backbone network and emergent aggregation operation becomes an economical and efficient technical tool for CAPD, which can be regarded as a compromise between FSL and WSLMIL.