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

TL;DR

This paper introduces TSEML, a novel meta-learning framework combining MAML and ProtoNet, to improve few-shot classification of cancer molecular subtypes using a newly constructed dataset, addressing data scarcity challenges.

Contribution

The paper proposes a task-specific embedding-based meta-learning method (TSEML) that effectively leverages knowledge from related tasks for cancer subtype classification in few-shot scenarios.

Findings

TSEML outperforms existing methods on the TCGA Few-Shot dataset.

The framework effectively captures diverse, fine-grained features.

Demonstrates improved accuracy in small-sample cancer subtype classification.

Abstract

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.