ConceptVAE: Self-Supervised Fine-Grained Concept Disentanglement from 2D Echocardiographies

• URL: http://arxiv.org/abs/2502.01335v1
• Date: Mon, 03 Feb 2025 13:18:01 GMT
• Title: ConceptVAE: Self-Supervised Fine-Grained Concept Disentanglement from 2D Echocardiographies
• Authors: Costin F. Ciusdel, Alex Serban, Tiziano Passerini,
• Abstract summary: ConceptVAE is a novel pre-training framework that detects and disentangles fine-grained concepts from their style characteristics in a self-supervised manner.<n>We present a suite of loss terms and model architecture primitives designed to discretise input data into a preset number of concepts along with their local style.<n>We validate ConceptVAE both qualitatively and quantitatively, demonstrating its ability to detect fine-grained anatomical structures such as blood pools and septum walls from 2D cardiac echocardiographies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While traditional self-supervised learning methods improve performance and robustness across various medical tasks, they rely on single-vector embeddings that may not capture fine-grained concepts such as anatomical structures or organs. The ability to identify such concepts and their characteristics without supervision has the potential to improve pre-training methods, and enable novel applications such as fine-grained image retrieval and concept-based outlier detection. In this paper, we introduce ConceptVAE, a novel pre-training framework that detects and disentangles fine-grained concepts from their style characteristics in a self-supervised manner. We present a suite of loss terms and model architecture primitives designed to discretise input data into a preset number of concepts along with their local style. We validate ConceptVAE both qualitatively and quantitatively, demonstrating its ability to detect fine-grained anatomical structures such as blood pools and septum walls from 2D cardiac echocardiographies. Quantitatively, ConceptVAE outperforms traditional self-supervised methods in tasks such as region-based instance retrieval, semantic segmentation, out-of-distribution detection, and object detection. Additionally, we explore the generation of in-distribution synthetic data that maintains the same concepts as the training data but with distinct styles, highlighting its potential for more calibrated data generation. Overall, our study introduces and validates a promising new pre-training technique based on concept-style disentanglement, opening multiple avenues for developing models for medical image analysis that are more interpretable and explainable than black-box approaches.

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