V7-DiVA: a feature-based Deep MIL colorectal whole-slide histopathology research platform with blinded external...

Whole-slide imaging offers a route to scalable computational assessment of colorectal histology, but models developed on public cohorts may not transfer reliably to real-world clinical practice. We developed V7-DiVA (Version 7 Deep Integrated Visual Analytics), a feature-based, gated-attention deep multiple-instance learning (MIL) research platform that links slide-level classification with quality metadata and reviewable image regions.

Patch-level feature vectors were generated using a supervised feature extractor developed on NCT-CRC-HE-100K and independently evaluated on CRC-VAL-HE-7K. They were organized as 95-dimensional vectors comprising an 82-dimensional supervised feature core, tumor-evidence and morphology scores, coordinates, tissue fraction, and focus-quality features. The primary gated-attention Deep MIL model was developed on 1,972 TCGA-COAD/READ slides from 631 cases using case-grouped fivefold cross-validation. The trained model and its prespecified preprocessing specification, including standardization parameters estimated from TCGA, feature order, and inference rules, were then applied without modification to 300 de-identified local WSIs (150 malignant and 150 normal/non-neoplastic; one slide per case). A same-feature Transformer aggregator and a separately trained public-data-only CTransPath branch were secondary comparators. Scores were generated before reference-label mapping, and no local slide was used for model fitting, calibration, or threshold optimization.

Patch-level tumor-evidence AUROCs were 0.997 on NCT-CRC-HE-100K and 0.987 on CRC-VAL-HE-7K. The primary Deep MIL model yielded a case-grouped TCGA development AUROC of 0.991 (95% CI 0.980-0.999) and an external AUROC of 0.918 (0.882-0.950) after transfer without refitting. Slide-level quality metadata characterized differences in tissue extent and focus and provided review cues for misclassified slides.

V7-DiVA places feature-based gated-attention Deep MIL at the center of an integrated colorectal WSI research workflow. By learning from slide-level labels, aggregating variable-length feature bags, and retaining instance-level attention weights for review, the primary branch directly links weakly supervised learning to auditable image evidence. Its performance after transfer without refitting supports further multicenter evaluation, while the Transformer and CTransPath analyses provide complementary evidence across alternative aggregation and representation strategies. Independent calibration, broader diagnostic spectra, and prospective reader studies are required before clinical use.