LiteMIL: a computationally efficient cross-attention multiple instance learning for cancer subtyping on whole-slide images
Year: 2026
Type: article
Abstract: PurposeAccurate cancer subtyping is essential for precision medicine but challenged by the computational demands of gigapixel whole-slide images (WSIs). Although transformer-based multiple instance learning (MIL) methods achieve strong performance, their quadratic complexity limits clinical deployment. We introduce LiteMIL, a computationally efficient cross-attention MIL, optimized for WSIs classification.ApproachLiteMIL employs a single learnable query with multi-head cross-attention for bag-level aggregation from extracted features. We evaluated LiteMIL against five baselines (mean/max pooling, ABMIL, MAD-MIL, and TransMIL) on four TCGA datasets (breast: n=875, kidney: n=906, lung: n=958, and TUPAC16: n=821) using nested cross-validation with patient-level splitting. Systematic ablation studies evaluated multi-query variants, attention heads, dropout rates, and architectural components.ResultsLiteMIL achieved competitive accuracy (average 83.5%), matching TransMIL, while offering substantial efficiency gains: 4.8× fewer parameters (560K versus 2.67M), 2.9× faster inference (1.6s versus 4.6s per fold), and 6.7× lower Graphics Processing Unit (GPU) memory usage (1.15 GB versus 7.77 GB). LiteMIL excelled on lung (86.3% versus 85.0%), TUPAC16 (72% versus 71.4%), and matched kidney performance (89.9% versus 89.7%). Ablation studies revealed task-dependent multi-query performance benefits: Q=4 versus Q=1 improved morphologically heterogeneous tasks (breast/lung +1.3% each, p<0.05) but degraded on grading tasks (TUPAC16: −1.6%), validating single-query optimality for focused attention scenarios.ConclusionsLiteMIL provides a resource-efficient solution for WSI classification. The cross-attention architecture matches complex transformer performance while enabling deployment on consumer GPUs. Task-dependent design insights, single query for sparse discriminating features, multi-query for heterogeneous patterns, guide practical implementation. The architecture’s efficiency, combined with compact features, makes LiteMIL suitable for clinical integration in settings with limited computational infrastructure.
Source: Journal of medical imaging
Author: Haitham Kussaibi
Institution: Imam Abdulrahman Bin Faisal University
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7118973161
FWCI:
Cites:
Cited by:
Related to: