# Open-Set Recognition of Breast Cancer Treatments

arXiv: https://arxiv.org/abs/2201.02923 · HTML: https://mlsanity.com/paper/2201.02923/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 9 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Classification-Reconstruction Learning for Open-Set Recognition](https://mlsanity.com/paper/1812.04246/) (2018)
- [C2AE: Class Conditioned Auto-Encoder for Open-set Recognition](https://mlsanity.com/paper/1904.01198/) (2019)
- [Learning a Neural-network-based Representation for Open Set Recognition](https://mlsanity.com/paper/1802.04365/) (2018)
- [Sparse Representation-based Open Set Recognition](https://mlsanity.com/paper/1705.02431/) (2017)
- [Open-Set Recognition with Gaussian Mixture Variational Autoencoders](https://mlsanity.com/paper/2006.02003/) (2020)
- [Open Set Medical Diagnosis](https://mlsanity.com/paper/1910.02830/) (2019)

## Built on by (most cited first, 2 of 2)

- [Conformal Inference for Open-Set and Imbalanced Classification](https://mlsanity.com/paper/2510.13037/) (2025)
- [EVINET: Towards Open-World Graph Learning via Evidential Reasoning Network](https://mlsanity.com/paper/2506.07288/) (2025)

## Abstract (arXiv)

Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or "unknown." As novel cancer drug cocktails with improved treatment are continually discovered, predicting cancer treatments can naturally be formulated in terms of an open-set recognition problem. Drawbacks, due to modeling unknown samples during training, arise from straightforward implementations of prior work in healthcare open-set learning. Accordingly, we reframe the problem methodology and apply a recent existing Gaussian mixture variational autoencoder model, which achieves state-of-the-art results for image datasets, to breast cancer patient data. Not only do we obtain more accurate and robust classification results, with a 24.5% average F1 increase compared to a recent method, but we also reexamine open-set recognition in terms of deployability to a clinical setting.