# ConceptVAE: Self-Supervised Fine-Grained Concept Disentanglement from 2D Echocardiographies
## 1 Introduction  
Unsupervised and, in particular, Self-Supervised Learning (SSL) methods facilitate the use of unlabeled data to learn its underlying structure. These pre-training methods have demonstrated improved performance and robustness across a wide range of medical imaging tasks, outperforming models trained solely through supervised learning [1, 2, 3]. The core idea of SSL pre-training is to develop meaningful representations from input samples, represented as a single continuous embedding vector encapsulating the content displayed in an input [4]. These representations can be viewed as an aggregation of local concepts, their corresponding styles and their contribution on the overall meaning of the input. The nature of the representations learnt can vary depending on the specific method employed [5]. For example, some methods encourage the representations to be similar for similar or augmented input samples, and dissimilar for samples that depict distinctconcepts [6]. Other methods aim to ensure that the representations can be accurately reconstructed from partially masked inputs or features [7; 8]. Regardless of the approach employed, each method aims to develop a single-vector representation of the input, which may fail to capture fine-grained concepts present in it. For example, a 2D echocardiography of the heart can be broken down into concepts such as heart chambers, valves, and walls. However, the SSL methods' single-vector representation makes it challenging to discern whether such concepts are learned during pre-training [9; 10]. Moreover, similarity constraints imposed in SSL under various augmentations can cause algorithms to merge certain concepts and their associated styles. For example, two augmented views of the same input must produce similar representations. However, cropping or zooming can exclude some object parts from a view, while blurring or color jittering can alter local textures, making them different between the augmented views. This is one reason why SSL pre-trained models typically do not perform well on localized tasks, such as detecting localized pathologies, instance retrieval or Out-of-Distribution (OOD) detection [11; 12]. The ability to identify individual concepts that make up larger objects within input images, and capture particular traits of these concepts such as textures, will result in more expressive embeddings that can alleviate some of these weaknesses. In this paper, we present a novel pre-training method that learns to discretise an input image into a set of fine-grained concepts, and identifies a unique set of styles for each concept. Inspired by human perception, where the brain rapidly recognizes objects by first identifying essential concepts as key components and then perceiving detailed information like fine textures, our approach aims to mimic this process [13; 14; 15]. Using 2D cardiac echocardiographics, we show that the proposed method, which we term ConceptVAE and illustrate in Figure 1, can identify fine-grained concepts representing anatomical structures and regions such as heart chambers, walls or blood pools without any supervision. The main strength of our proposed framework is the concept (content)-style disentanglement that happens natively during the pretraining procedure, a behavior that doesn't occur within traditional SSL methods. We demonstrate the achievement of disentanglement and investigate its potential in a plurality of diverse downstream tasks (such as segmentation, object detection, retrieval, generation, outlier detection) where we directly exploit the proposed disentangled latent space. Applications in medical imaging, where as  
Figure 1: ConceptVAEoverview, where the blue blocks are trainable while the grey blocks are only updated using exponential moving average. pects such as model explainability and interpretability hold great interest, can benefit from concept-style disentanglement of the latent space.