# TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes

arXiv: https://arxiv.org/abs/2412.13228 · HTML: https://mlsanity.com/paper/2412.13228/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 17 Dec 2024.

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

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML](https://mlsanity.com/paper/1909.09157/) (2019)
- [Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning](https://mlsanity.com/paper/2003.04390/) (2020)
- [Advances and Challenges in Meta-Learning: A Technical Review](https://mlsanity.com/paper/2307.04722/) (2023)
- [Convolutional neural network models for cancer type prediction based on gene expression](https://mlsanity.com/paper/1906.07794/) (2019)
- [Few shot domain adaptation for in situ macromolecule structural classification in cryo-electron tomograms](https://mlsanity.com/paper/2007.15422/) (2020)
- [Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction](https://mlsanity.com/paper/2009.00792/) (2020)
- [Cancer Subtyping by Improved Transcriptomic Features Using Vector Quantized Variational Autoencoder](https://mlsanity.com/paper/2207.09783/) (2022)

## Abstract (arXiv)

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA Few-Shot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification.