# Magnification Generalization for Histopathology Image Embedding

arXiv: https://arxiv.org/abs/2101.07757 · HTML: https://mlsanity.com/paper/2101.07757/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence](https://mlsanity.com/paper/1911.08736/) (2019)
- [A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning](https://mlsanity.com/paper/1904.08720/) (2019)
- [Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks](https://mlsanity.com/paper/2004.04674/) (2020)
- [Recognizing Magnification Levels in Microscopic Snapshots](https://mlsanity.com/paper/2005.03748/) (2020)

## Built on by (most cited first, 5 of 5)

- [Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images](https://mlsanity.com/paper/2203.07707/) (2022)
- [Generalizing Nucleus Recognition Model in Multi-source Images via Pruning](https://mlsanity.com/paper/2107.02500/) (2021)
- [Hospital-Agnostic Image Representation Learning in Digital Pathology](https://mlsanity.com/paper/2204.02404/) (2022)
- [Histo-MExNet: A Unified Framework for Real-World, Cross-Magnification, and Trustworthy Breast Cancer Histopathology](https://mlsanity.com/paper/2603.14416/) (2026)
- [Magnification-Invariant Image Classification via Domain Generalization and Stable Sparse Embedding Signatures](https://mlsanity.com/paper/2604.25817/) (2026)

## Abstract (arXiv)

Histopathology image embedding is an active research area in computer vision. Most of the embedding models exclusively concentrate on a specific magnification level. However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level. Two main approaches for tackling this goal are domain adaptation and domain generalization, where the target magnification levels may or may not be introduced to the model in training, respectively. Although magnification adaptation is a well-studied topic in the literature, this paper, to the best of our knowledge, is the first work on magnification generalization for histopathology image embedding. We use an episodic trainable domain generalization technique for magnification generalization, namely Model Agnostic Learning of Semantic Features (MASF), which works based on the Model Agnostic Meta-Learning (MAML) concept. Our experimental results on a breast cancer histopathology dataset with four different magnification levels show the proposed method's effectiveness for magnification generalization.