mlsanityData to 14 Oct 2026

A Deployment-Friendly Foundational Framework for Efficient Computational Pathology

arXiv 2602.14010 ↗First posted 15 Feb 2026cs.CV cs.AIcited by 1

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Abstract (arXiv)

Pathology foundation models (PFMs) generalize well across computational pathology tasks but remain costly for gigapixel whole-slide image analysis. Here, we present LitePath, a deployment-friendly framework that addresses model over-parameterization and patch-level redundancy. LitePath combines LiteFM, a compact model distilled from Virchow2, H-Optimus-1 and UNI2 using 190 million patches, with the Adaptive Patch Selector for task-specific patch selection. Compared with Virchow2, LitePath uses 28x fewer parameters and 403.5x fewer FLOPs. On an NVIDIA Jetson Orin Nano Super, it processes 208 slides per hour, 104.5x faster than Virchow2, and consumes 0.36 kWh per 3,000 slides, 171x less energy than Virchow2 on an RTX 3090 GPU. We evaluated LitePath on 45 multicenter cohorts across four organs and 33 tasks, comprising 37 classification and 8 survival cohorts, including 33 internal, 10 external and 2 prospective cohorts, with 17,837 slides from 9,977 patients disjoint from the pretraining data. Among 22 PFMs, LitePath achieved the best average rank (6.56 vs. 6.58 for Virchow2), retained 99.71% of Virchow2's Macro-AUC across classification cohorts, and improved mean C-index by 2.14 percentage points across survival cohorts (71.91% vs. 69.77%). We further introduce the Deployability Score (D-Score), a weighted geometric mean of normalized task performance and FLOPs. LitePath achieved the highest D-Score (0.8455), outperforming H0-mini (0.7723) and Virchow2 (0.7297). In a randomized paired crossover study of four pathologists and 120 cases, LitePath increased diagnostic accuracy by 4.1-15.8 percentage points and reduced diagnostic time by 10.9-14.2%. These results demonstrate rapid, cost-effective and energy-efficient pathology image analysis on accessible hardware while maintaining competitive performance.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 31 of its 66 references that are in the map. First posted on arXiv 15 Feb 2026.

Builds on

31 of 66 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  5. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  6. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  7. 2020Training data-efficient image transformers & distillation through attentioncited by 4,586
  8. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  9. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  10. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  11. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 256
  12. 2024Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathologycited by 161
19 more
  1. 2021BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Imagescited by 132
  2. 2018BACH: Grand Challenge on Breast Cancer Histology Imagescited by 131
  3. 2023Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Imagescited by 91
  4. 2024Phikon-v2, A large and public feature extractor for biomarker predictioncited by 74
  5. 2019CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentationcited by 73
  6. 2021Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detectioncited by 68
  7. 2023Attention-Challenging Multiple Instance Learning for Whole Slide Image Classificationcited by 61
  8. 2024A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Modelcited by 59
  9. 2021Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articlescited by 58
  10. 2024Hibou: A Family of Foundational Vision Transformers for Pathologycited by 57
  11. 2021Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slidescited by 43
  12. 2023OCELOT: Overlapped Cell on Tissue Dataset for Histopathologycited by 29
  13. 2023Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challengecited by 25
  14. 2025PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncologycited by 24
  15. 2021DiagSet: a dataset for prostate cancer histopathological image classificationcited by 13
  16. 2023The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissuecited by 11
  17. 2020DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image setcited by 8
  18. 2022TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challengecited by 8
  19. 2025Towards Computation- and Communication-efficient Computational Pathologycited by 1

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