PyraE2E: Enhancing End-to-End WSI Analysis via Cross-Scale Super-Resolution

Abstract

Offline feature extraction with pretrained encoders followedby multiple instance learning (MIL) aggregation is still the dominantparadigm for whole slide image (WSI) analysis, but the domain gap be-tween natural and histopathology images limits representation quality.End-to-end optimization can reduce this gap by training the encoder withslide-level objectives, but it often suffers from (i) cost-driven fine-grainedand morphological information loss caused by low-magnification small in-puts and random patch sampling, and (ii) sparse supervision from slidelabels alone. We propose PyraE2E, an end-to-end framework that turnsthe intrinsic multi-resolution WSI pyramid into a dense self-supervisionsignal via super-resolution (SR). For each sampled low-resolution (LR)patch at low magnification, we use its spatially aligned high-resolution(HR) patch at higher magnification as a reconstruction target, providingcross-scale pixel-level supervision while keeping computation boundedby operating on LR inputs. PyraE2E combines an embedded Cluster-Score Sampling module to select informative LR regions with a sharedGlobal–Local Partitioned (GLP) encoder whose HR-informed featuresare jointly optimized by an SR reconstruction head and a slide-level pre-diction head. Joint training couples dense cross-scale supervision withslide-level objectives, improving end-to-end representations and down-stream WSI prediction under controlled computational cost.