# Prototypical Clustering Networks for Dermatological Disease Diagnosis

arXiv: https://arxiv.org/abs/1811.03066 · HTML: https://mlsanity.com/paper/1811.03066/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [A Survey on Long-Tailed Visual Recognition](https://mlsanity.com/paper/2205.13775/) (2022)
- [Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions](https://mlsanity.com/paper/2104.03829/) (2021)
- [Meta-Learning with Fewer Tasks through Task Interpolation](https://mlsanity.com/paper/2106.02695/) (2021)
- [No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets](https://mlsanity.com/paper/2109.05554/) (2021)
- [Federated Reconnaissance: Efficient, Distributed, Class-Incremental Learning](https://mlsanity.com/paper/2109.00150/) (2021)
- [Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space](https://mlsanity.com/paper/2203.08549/) (2022)

## Abstract (arXiv)

We consider the problem of image classification for the purpose of aiding doctors in dermatological diagnosis. Dermatological diagnosis poses two major challenges for standard off-the-shelf techniques: First, the data distribution is typically extremely long tailed. Second, intra-class variability is often large. To address the first issue, we formulate the problem as low-shot learning, where once deployed, a base classifier must rapidly generalize to diagnose novel conditions given very few labeled examples. To model diverse classes effectively, we propose Prototypical Clustering Networks (PCN), an extension to Prototypical Networks that learns a mixture of prototypes for each class. Prototypes are initialized for each class via clustering and refined via an online update scheme. Classification is performed by measuring similarity to a weighted combination of prototypes within a class, where the weights are the inferred cluster responsibilities. We demonstrate the strengths of our approach in effective diagnosis on a realistic dataset of dermatological conditions.