Digital Pathology Models

• Digital pathology models are computational frameworks that quantitatively analyze whole-slide images using architectures like CNNs and ViTs for diagnostics and research.
• They integrate advanced techniques such as self-supervised pretraining, structured state-space models, and multiple instance learning to process diverse tissue data efficiently.
• Emerging methods emphasize dynamic adaptation, robust data normalization, and explainability to improve clinical applications like cancer subtyping and biomarker prediction.

Digital pathology models are computational frameworks designed to quantitatively analyze digitized whole-slide images (WSIs) of stained tissue specimens, enabling automated diagnostics, subtyping, biomarker prediction, survival estimation, and diverse downstream research and clinical tasks. These models span classic convolutional neural networks (CNNs), vision transformers (ViTs), structured state space models, and contemporary self-supervised and generative paradigms. State-of-the-art approaches leverage foundation models pretrained on large-scale, domain-specific pathology datasets using self-distillation, contrastive learning, and hybrid objectives. The growing diversity of histological stains, scanning protocols, and clinical requirements presents multifaceted challenges in model robustness, scalability, and interpretability, driving active research into dynamic adaptation, generative data augmentation, and explainable AI within the digital pathology domain.

1. Core Architectures and Training Paradigms

Digital pathology models primarily utilize high-capacity deep learning architectures tailored to accommodate gigapixel WSIs and the peculiarities of histopathological image data.

• Convolutional Neural Networks (CNNs): Early and still widely used (e.g., ResNet-50, DenseNet, KimiaNet), CNNs process small image patches to extract local morphological features. Standard CNNs lack built-in invariance to rotation and reflection, a limitation addressed using rotation equivariant CNNs (e.g., p4m-DenseNet, D₄/C₈ group convolution layers), yielding improved stability and generalization for tasks such as tumor detection in lymph node metastases (Veeling et al., 2018).
• Vision Transformers (ViTs): ViTs, such as ViT-B/16, ViT-H/14, and ViT-G/14, process patches as independent tokens via multi-head self-attention, enabling capture of broader tissue context and modeling long-range architectural relationships. Large ViT models (e.g., Virchow: 632M params (Vorontsov et al., 2023); Atlas: 632M (Alber et al., 9 Jan 2025)) dominate the current digital pathology foundation model landscape.
• Self-Supervised Pretraining: Models are pretrained using objectives such as DINOv2 (student-teacher self-distillation with multi-crop augmentations), iBOT (mask-prediction), contrastive learning (CLIP/PLIP for vision–language alignment), and hybrid loss terms. These settings enable learning from millions of unlabeled WSIs and their patches, capturing variations in stain, morphology, and site (Vorontsov et al., 2023, Yan et al., 31 Mar 2025, Filiot et al., 27 Jan 2025).
• Structured State Space Models (S4): To aggregate long sequences of patch embeddings in WSIs, S4 models replace attention- or RNN-based bag-level learners with efficient, memory-preserving linear recurrent architectures, offering O(L·log L) inference for slides with tens of thousands of patches (Fillioux et al., 2023).
• Domain-Specific Data Curation: Foundation models (Atlas (Alber et al., 9 Jan 2025), PathOrchestra (Yan et al., 31 Mar 2025), EXAONE Path 2.0 (Pyeon et al., 9 Jul 2025)) utilize institutional-scale data infrastructures, comprising hundreds of thousands to over a million WSIs across dozens of tissue types, stains, and scanner vendors. Stain normalization protocols (Macenko, Reinhard) are frequently adopted to mitigate color variation and WSI-specific feature collapse (Yun et al., 1 Aug 2024, Kömen et al., 22 Jul 2025).

2. Multiple Instance Learning and Aggregation Techniques

Whole-slide image analysis in digital pathology imposes unique computational and statistical challenges due to the size and heterogeneity of WSIs.

• Patch Extraction: WSIs are tessellated into patches (typically 224×224, 256×256, or 512×512 px), and background/tissue detection removes irrelevant regions.
• Patch-level Embedding: Extracted patches are processed by a backbone (CNN or ViT), producing fixed-dimensional feature vectors.
• Bag-level Aggregation: Because slide-level supervision is sparse, multiple instance learning (MIL) frameworks (AttentionMIL [Ilse et al.], ABMIL, TransMIL, CLAM) aggregate patch embeddings into a slide-level representation:
  • Mean-pooling, max-pooling
  • Attention-based pooling: $$a_i = \frac{\exp(w^\top z_i)}{\sum_{j=1}^N \exp(w^\top z_j)},\ z_\text{slide} = \sum_{i=1}^N a_i z_i$$
  • Transformer-based (self-attention) aggregators or state-space sequence models (S4) for ultra-long patch sequences (Bredell et al., 2023, Fillioux et al., 2023, Meseguer et al., 21 Oct 2024, Papadopoulos et al., 20 May 2024)
• Downstream Heads: Small MLP or linear classifiers are trained on pooled slide representations for tasks such as cancer subtyping, mutation status prediction, or survival analysis.

Hyperparameter sweeps for aggregator design (learning rate, bag size, depth, dropout) are essential for fair benchmarking of feature extractors, as downstream configuration sensitivity can bias performance comparisons significantly (Bredell et al., 2023).

3. Model Adaptation, Robustness, and Efficiency

Recent advances focus on addressing the limitations of static foundation models and the challenges associated with domain shifts.

• Dynamic Feature Learning (PathFiT): PathFiT enables plug-and-play dynamic adaptation by freezing pretrained attention blocks and inserting low-rank, trainable "delta" matrices ($\Delta W = B A$), allowing feature space re-embedding with minimal parameter overhead (~3–6% of full backbone), significantly improving adaptability to rare cancers, specialized staining, and out-of-distribution tasks (Li et al., 29 Dec 2024).
• Distillation and Lightweight Models: Distillation compresses large FMs (H-Optimus-0, 1.1B) down to ViT-Base scale (H0-mini, 86M) with joint DINO/iBOT training, maintaining accuracy (e.g., HEST Pearson 0.4044 for H0-mini vs. 0.4224 for H-Optimus-0) and significantly increasing inference speed and robustness to stain/scanner variation (Filiot et al., 27 Jan 2025).
• Robustness Evaluation (PathoROB): Three metrics are introduced: robustness index $R_k$, performance-drop index (APD), and adjusted Rand index-based clustering score. Robust representations (e.g., Virchow2, Atlas) prioritize biological information over center/scanner artifacts, essential for generalization and clinically safe deployment. Techniques such as Reinhard normalization, ComBat batch-correction, and domain adversarial training (DANN) provide post-hoc robustification (Kömen et al., 22 Jul 2025).
• WSI-Specific Feature Collapse: Self-supervised patch models tend to cluster features by WSI origin due to color/protocol artifacts. Macenko stain normalization throughout pretraining (EXAPath) largely attenuates this, with intra/inter-WSI variance ratio $R_\text{exa}=0.42$ (vs. $R_\text{wo}=0.05$ without normalization), resulting in parameter/data-efficient generalization (Yun et al., 1 Aug 2024).
• Synthetic Data Generation: Prototype-guided latent diffusion synthesizes high-fidelity, diverse histology patches anchored to learned morphologic prototypes, enabling SSL pipelines that achieve SOTA subtyping and survival accuracy with ~60–760× less real data than standard FMs (Redekop et al., 15 Apr 2025).

4. Downstream Clinical and Research Applications

Digital pathology models underpin a wide spectrum of critical tasks in both research and clinical settings:

• Cancer Detection and Subtyping: PANDA (prostate), CRC-100K (colorectal), BACH (breast) benchmarks demonstrate SOTA balanced accuracies (e.g., Atlas: CRC-100k 97.1%, BACH 93.1%) (Alber et al., 9 Jan 2025).
• Biomarker and Gene Expression Prediction: HEST-COAD, HEST-LUAD, and spatial transcriptomics tasks employ frozen FM features with linear probing/regression for multi-gene expression or MSI/FGFR3/EGFR prediction, e.g., PathOrchestra exceeds prior SOTA by 4–7% in gene-expression prediction (Yan et al., 31 Mar 2025).
• Content-Based Image Retrieval and QA: Efficient indexing pipelines such as SPLICE select representative (non-redundant) patches per WSI (collage size ~10–15 patches), yielding ≈50–60% storage savings over prior mosaic approaches (Yottixel) while matching or improving retrieval accuracy (Alsaafin et al., 26 Apr 2024). Deep learning-based QA models (InceptionResNet) for artifact detection (air bubbles, folds) streamline manual review and facilitate robust downstream diagnostics (VandeHaar et al., 12 Jun 2025).
• Multi-modal and Multi-task Reporting: PathOrchestra's structured report pipeline aggregates discrete subtask outputs (e.g., subtype, 29 IHC marker statuses) into a unified machine-readable report, marking a step toward comprehensive, automated digital pathology reporting (Yan et al., 31 Mar 2025).

5. Explainability, Interpretability, and Clinical Integration

Interpretable models are crucial for clinical deployment; recent efforts address the black-box nature of deep models:

• Clustering Internal Activations: Clustering feature-map activations via non-negative matrix factorization (NMF) yields multi-pattern, globally interpretable overlays, mapping clusters to morphologically coherent tissue compartments (e.g., cancer epithelium, stroma, gland lumen). This approach outperforms pixel-wise saliency maps (GradCAM), aligning better with pathologist intuition and facilitating training and trust (Bajger et al., 18 Nov 2025).
• Attention Heatmaps: Attention-based MIL frameworks provide heatmaps highlighting slide regions most influential for the diagnosis, aiding human verification and rapid triage (Meseguer et al., 21 Oct 2024).
• Transparent Retrieval: Integration of foundation models with CBIR/IR systems grounds AI predictions in concrete retrieved cases, counteracting hallucinations and black-box reasoning (Tizhoosh, 13 Mar 2024).
• Semi-Automated Quality Control: Deep ensemble pipelines flag and localize artifacts, reduce manual QA workload by an estimated 60%, and allow pathologist feedback annotation, closing the loop for continual model improvement (VandeHaar et al., 12 Jun 2025).

6. Current Limitations and Future Directions

• Domain Shift and Generalization: Even high-performing FMs can spuriously learn technical confounders (e.g., scanner, lab-induced stain), which may result in catastrophic misclassification in external or rare-site datasets. Robustness benchmarks (PathoROB) and post-hoc correction techniques remain essential (Kömen et al., 22 Jul 2025).
• Extreme Scale and Few-shot Regimes: Training on ever-larger, multi-institutional WSI archives is driving performance. However, models such as EXAONE Path 2.0 show that hierarchical, curriculum-trained, end-to-end supervised transformers can reach SOTA with far fewer slides, offering a data-efficient alternative (Pyeon et al., 9 Jul 2025).
• Explainability and Regulatory Approval: Development of interpretable models and transparent QA procedures are required for regulatory acceptance and mainstream clinical deployment (Bajger et al., 18 Nov 2025, BenTaieb et al., 2019).
• Multi-modal Foundation Models: Integration of histopathology, textual pathology reports, genomics, and radiology is an emerging strategy; vision–LLMs (PLIP, CONCH) and LVLMs promise richer representations and scalable generalization (Meseguer et al., 21 Oct 2024, Tizhoosh, 13 Mar 2024).
• End-to-End WSI Models and Efficient Indexing: Direct processing of full WSIs (versus patch aggregation) and development of compact, rapid retrieval structures (e.g., SPLICE) are ongoing research frontiers (Alsaafin et al., 26 Apr 2024).
• Synthetic Data and Stain-invariant Learning: Generative AI (GANs, diffusion) and stain normalization pipelines are being actively investigated for scaling model diversity, boosting rare-class performance, and harmonizing data (Yun et al., 1 Aug 2024, Redekop et al., 15 Apr 2025).

7. Summary Table: Recent Digital Pathology Foundation Models

Model	Backbone	Data Scale (WSIs)	Pretraining	Key Features	Benchmark Highlights
Atlas	ViT-H/14	1.2M	DINOv2/RudolfV	Multi-scale, multi-stain, efficient	State-of-the-art on 21 tasks
Virchow	ViT-H/14	1.5M	DINOv2	Gigapixel, pan-cancer, OOD robust	Specimen-level AUC=0.949
PathOrchestra	ViT-B/32	300K	DINOv2/iBOT/KoLeo	112 clinical tasks, structured rep.	AUC > 0.950 in 47/112 tasks
EXAONE Path 1.0	ViT-B/16	35K	DINOv1, stain-norm	WSI-collapse mitigation, efficient	Avg acc. 0.861, 86M params
EXAONE Path 2.0	ViT-HIPT	37K	End-to-end sup.	Curriculum, multi-task, hierarchical	Avg AUROC 0.784, top-3/all 10
H0-mini	ViT-B/14	43M tiles	Distilled DINO/iBOT	86M params, robust, efficient	HEST Pearson=0.4044
SPLICE (editor)	Patch subset	N/A	Unsupervised	Efficient WSI retrieval, patch select	Storage ↓ 60%, accuracy ↑

These representative models illustrate ongoing trends toward scalable, robust, and explainable digital pathology systems, with an emphasis on foundation model pretraining, efficient adaptation, synthetic data, and clinical integration.