# Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations

arXiv: https://arxiv.org/abs/2008.05571 · HTML: https://mlsanity.com/paper/2008.05571/

## Key facts

- Cited by 89 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 30 of 89)

- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images](https://mlsanity.com/paper/2306.07831/) (2023)
- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [Extending the WILDS Benchmark for Unsupervised Adaptation](https://mlsanity.com/paper/2112.05090/) (2021)
- [Self-supervised learning methods and applications in medical imaging analysis: A survey](https://mlsanity.com/paper/2109.08685/) (2021)
- [Transcriptomics-guided Slide Representation Learning in Computational Pathology](https://mlsanity.com/paper/2405.11618/) (2024)
- [Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology](https://mlsanity.com/paper/2203.00585/) (2022)
- [A General-Purpose Self-Supervised Model for Computational Pathology](https://mlsanity.com/paper/2308.15474/) (2023)
- [NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation](https://mlsanity.com/paper/2102.09099/) (2021)
- [Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Images](https://mlsanity.com/paper/2202.07001/) (2022)
- [Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis](https://mlsanity.com/paper/2208.08789/) (2022)
- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [Multistain Pretraining for Slide Representation Learning in Pathology](https://mlsanity.com/paper/2408.02859/) (2024)
- [Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology](https://mlsanity.com/paper/2109.03299/) (2021)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers](https://mlsanity.com/paper/2109.05819/) (2021)
- [ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification](https://mlsanity.com/paper/2202.07570/) (2022)
- [CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment](https://mlsanity.com/paper/2406.05205/) (2024)
- [Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification](https://mlsanity.com/paper/2306.16180/) (2023)
- [Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarios](https://mlsanity.com/paper/2410.16038/) (2024)
- [Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training](https://mlsanity.com/paper/2202.08195/) (2022)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning](https://mlsanity.com/paper/2411.13623/) (2024)
- [Improving Self-supervised Learning with Hardness-aware Dynamic Curriculum Learning: An Application to Digital Pathology](https://mlsanity.com/paper/2108.07183/) (2021)
- [Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications](https://mlsanity.com/paper/2112.05760/) (2021)
- [Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs](https://mlsanity.com/paper/2205.04282/) (2022)
- [ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images](https://mlsanity.com/paper/2207.06733/) (2022)
- [Learning image representations for anomaly detection: application to discovery of histological alterations in drug development](https://mlsanity.com/paper/2210.07675/) (2022)
- [Domain-specific optimization and diverse evaluation of self-supervised models for histopathology](https://mlsanity.com/paper/2310.13259/) (2023)
- [Resolution-Based Distillation for Efficient Histology Image Classification](https://mlsanity.com/paper/2101.04170/) (2021)

## Abstract (arXiv)

While high-resolution pathology images lend themselves well to `data hungry' deep learning algorithms, obtaining exhaustive annotations on these images is a major challenge. In this paper, we propose a self-supervised CNN approach to leverage unlabeled data for learning generalizable and domain invariant representations in pathology images. The proposed approach, which we term as Self-Path, is a multi-task learning approach where the main task is tissue classification and pretext tasks are a variety of self-supervised tasks with labels inherent to the input data. We introduce novel domain specific self-supervision tasks that leverage contextual, multi-resolution and semantic features in pathology images for semi-supervised learning and domain adaptation. We investigate the effectiveness of Self-Path on 3 different pathology datasets. Our results show that Self-Path with the domain-specific pretext tasks achieves state-of-the-art performance for semi-supervised learning when small amounts of labeled data are available. Further, we show that Self-Path improves domain adaptation for classification of histology image patches when there is no labeled data available for the target domain. This approach can potentially be employed for other applications in computational pathology, where annotation budget is often limited or large amount of unlabeled image data is available.