# ConceptVAE: Self-Supervised Fine-Grained Concept Disentanglement from 2D Echocardiographies
## 2 Related Work  
We identify a series of related works that can be categorized into three distinct groups: (i) SSL methods, encompassing both general approaches from natural images and those specific to medical images [4; 16]; (ii) Disentangled Representation Learning (DRL) methods, which aim to train models capable of identifying and mapping factors of variation to semantically meaningful variables [17; 18]; and (iii) the application of SSL methods to improve performance in medical image processing tasks related to 2D echocardiographies, such as segmentation or information retrieval. Below, we discuss these groups independently and explore their interplay. The primary SSL methods can be categorized in (i) contrastive learning methods (e.g., [19; 20]), which aim to create similar representations for input images showing the same objects and contrastive representations for images showing different objects; (ii) correlation-based methods (e.g., [21]), which aim to preserve the variance of the embeddings while decorrelating variables related to distinct objects; and (iii) masked image modeling methods (e.g., [22; 23]), which aim to reconstruct the original input from its masked version. Recent studies indicate that, despite differences in methodology and training objectives, contrastive and correlation-based methods are closely related and may yield similar results, as they minimize criteria that are equivalent under certain conditions [24]. All methods in these groups focus on developing single-vector (and not local or concept-based) representations, which can be used in distinct downstream tasks. Within SSL methods, some approaches yield models with interesting emergent properties. For example, vision transformer models [25] trained with DINO [26; 27] can generate features that explicitly describe the semantic segmentation of an image. These features can be directly linked to actual objects present in the image, which can be broadly interpreted as independent concepts. Training with DINO improves performance in image classification, segmentation, and even information retrieval. Building upon DINO, ref. [28] associated a fixed number of prototypical concepts with the semantics of each image using a pixel assignment scheme based on k-means clustering, further enhancing semantic segmentation. Despite the fact that global representations developed through SSL methods can linearly separate certain object classes, these methods do not ensure that the learned latent space structure is meaningful. Specifically, intermediate feature maps (i.e., the spatial feature maps before the final projector head) may not be sufficiently descriptive to reliably differentiate between similar visual concepts or to group together representations of objects from the same class. Additionally, these representations might either be intertwined with style information or attempt to suppress it to achieve invariance against train-time augmentations [18]. In contrast, DRL is a family of training methods aimed at isolating the factors of variation driving the generative process behind a data distribution into distinct latent variables. Refs. [18; 29] provide overviews of recent techniques in DRL. Among various benefits, DRL can improve a model's explainability, controllability, and robustness [29]. Nevertheless, DRL methods often need labels to learn meaningful representations [30] and have limited applicability to image-based tasks, primarily focusing on image generation [29]. In contrast, ConceptVAE is designed as a general pre-training strategy that benefits multiple downstream tasks. Within DRL, ConceptVAE is similar to _content-style disentanglement_[18], as it deliberately assigns distinct roles to different components of the latent space. For example, certain components represent anatomical concepts such as heart valves (acting as the _content_), while others capture their local specifics (acting as the _style_). Our model uses both discrete and continuous latent variables, for the content and style of input images, respectively.