# Robust Tumor Detection from Coarse Annotations via Multi-Magnification Ensembles

arXiv: https://arxiv.org/abs/2303.16533 · HTML: https://mlsanity.com/paper/2303.16533/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [Deep Ensembles: A Loss Landscape Perspective](https://mlsanity.com/paper/1912.02757/) (2019)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks](https://mlsanity.com/paper/1511.06314/) (2015)
- [Deep Learning for Identifying Metastatic Breast Cancer](https://mlsanity.com/paper/1606.05718/) (2016)
- [Detecting Cancer Metastases on Gigapixel Pathology Images](https://mlsanity.com/paper/1703.02442/) (2017)
- [A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis](https://mlsanity.com/paper/2001.00258/) (2020)
- [Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histology](https://mlsanity.com/paper/2012.03583/) (2020)
- [Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation](https://mlsanity.com/paper/1910.13042/) (2019)
- [Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images](https://mlsanity.com/paper/2109.10778/) (2021)

## Abstract (arXiv)

Cancer detection and classification from gigapixel whole slide images of stained tissue specimens has recently experienced enormous progress in computational histopathology. The limitation of available pixel-wise annotated scans shifted the focus from tumor localization to global slide-level classification on the basis of (weakly-supervised) multiple-instance learning despite the clinical importance of local cancer detection. However, the worse performance of these techniques in comparison to fully supervised methods has limited their usage until now for diagnostic interventions in domains of life-threatening diseases such as cancer. In this work, we put the focus back on tumor localization in form of a patch-level classification task and take up the setting of so-called coarse annotations, which provide greater training supervision while remaining feasible from a clinical standpoint. To this end, we present a novel ensemble method that not only significantly improves the detection accuracy of metastasis on the open CAMELYON16 data set of sentinel lymph nodes of breast cancer patients, but also considerably increases its robustness against noise while training on coarse annotations. Our experiments show that better results can be achieved with our technique making it clinically feasible to use for cancer diagnosis and opening a new avenue for translational and clinical research.