HERO: Histology Encoder for Robust Representation in Oncology

arXiv:2609.35943 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-10-01

Code: https://github.com/bioptimus/releases

Project page: https://wearewaiv.github.io/histoboard

Terminology

• Foundation Model — 52× in this paper · explained in 3 episodes
• Benchmark — 46× in this paper · explained in 2 episodes
• Robustness — 38× in this paper · explained in 5 episodes
• Foundation Models — 29× in this paper · explained in 7 episodes
• Histology Encoder for Robust Representation in Oncology — 23× in this paper
• Embeddings — 17× in this paper · explained in 2 episodes
• Calibration — 13× in this paper · explained in 7 episodes
• Accuracy — 10× in this paper · explained in 1 episode
• Adam — 6× in this paper · explained in 2 episodes
• Linear probe — 6× in this paper · explained in 2 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Clustering — 4× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Self-Supervised Learning — 4× in this paper · explained in 8 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Linear Probing — 3× in this paper · explained in 2 episodes
• AUROC — 2× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adversarial Training — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Data Pruning — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Nadia — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes

Sources

• SemDeDup: Data-efficient learning at web-scale through semantic deduplication
• Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit'e, and Aignostics
• Atlas 2 -- Foundation models for clinical deployment
• Causal multi-modal AI for personalized chemosensitivity prediction
• Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images
• Emerging Properties in Self-Supervised Vision Transformers
• Vision Transformers Need Registers
• Current Pathology Foundation Models are unrobust to Medical Center Differences
• RudolfV: A Foundation Model by Pathologists for Pathologists
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Phikon-v2, A large and public feature extractor for biomarker prediction
• PanNuke Dataset Extension, Insights and Baselines
• Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts
• PLUTO: Pathology-Universal Transformer
• Towards Large-Scale Training of Pathology Foundation Models
• Do Histopathological Foundation Models Eliminate Batch Effects? A Comparative Study
• Beyond Diagnostic Performance: Revealing and Quantifying Ethical Risks in Pathology Foundation Models
• THUNDER: Tile-level Histopathology image UNDERstanding benchmark
• Mind the Gap: Continuous Magnification Sampling for Pathology Foundation Models
• Hibou: A Family of Foundational Vision Transformers for Pathology

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models