Deep learning models have emerged as the cornerstone of medical image segmentation, but their efficacy hinges on the availability of extensive manually labeled datasets and their adaptability to unforeseen categories remains a challenge. Few-shot segmentation (FSS) offers a promising solution by endowing models with the capacity to learn novel classes from limited labeled examples. A leading method for FSS is ALPNet, which compares features between the query image and the few available support segmented images. A key question about using ALPNet is how to design its features. In this work, we delve into the potential of using features from DINOv2, which is a foundational self-supervised learning model in computer vision. Leveraging the strengths of ALPNet and harnessing the feature extraction capabilities of DINOv2, we present a novel approach to few-shot segmentation that not only enhances performance but also paves the way for more robust and adaptable medical image analysis.

Abstract: Recently self supervised learning has seen explosive growth and use in variety of machine learning tasks because of its ability to avoid the cost of annotating large-scale datasets. This paper gives an overview for best self supervised learning approaches for multimodal learning. The presented approaches have been aggregated by extensive study of the literature and tackle the application of self supervised learning in different ways. The approaches discussed are cross modal generation, cross modal pretraining, cyclic translation, and generating unimodal labels in self supervised fashion. Abstract: Multimodal pre-training breaks down the modality barriers and allows the individual modalities to be mutually augmented with information, resulting in significant advances in representation learning.

In recent years, the AI field has made tremendous progress in developing AI systems that can learn from massive amounts of carefully labeled data. This paradigm of supervised learning has a proven track record for training specialist models that perform extremely well on the task they were trained to do. Unfortunately, there's a limit to how far the field of AI can go with supervised learning alone. Supervised learning is a bottleneck for building more intelligent generalist models that can do multiple tasks and acquire new skills without massive amounts of labeled data. Practically speaking, it's impossible to label everything in the world.

Self Supervised Learning is an interesting research area where the goal is to learn rich representations from unlabeled data without any human annotation. This can be achieved by creatively formulating a problem such that you use parts of the data itself as labels and try to predict that. Such formulations are called pretext tasks. For example, you can setup a pretext task to predict the color version of the image given the grayscale version. Similarly, you could remove a part of the image and train a model to predict the part from the surrounding.

Deep learning has truly reshuffled things in machine learning field, and specifically in image recognition tasks. In 2012, Alex-net has initiated a (still far from ending) race towards solving, or at least significantly improving, computer vision tasks. Each of these research paths improves training quality (speed, accuracy, sometimes generalization), but it seems that doing more of the same thing may result in some gradual improvements, but not a in significant breakthrough. On the other hand, growing body of work in deep learning shows that there are significant flaws in current methods, especially in terms of generalization, e.g this recent one: generalization failure when objects are rotated: So there seems to be a need of improvements that are a bit more aggressive. Or perhaps expanding the research spectrum to ideas that may be a bit riskier.

Deep learning has truly reshuffled things in machine learning field, and specifically in image recognition tasks. In 2012, Alex-net has initiated a (still far from ending) race towards solving, or at least significantly improving, computer vision tasks. Each of these research paths improves training quality (speed, accuracy, sometimes generalization), but it seems that doing more of the same thing may result in some gradual improvements, but not a in significant breakthrough. On the other hand, growing body of work in deep learning shows that there are significant flaws in current methods, especially in terms of generalization, e.g this recent one: generalization failure when objects are rotated: So there seems to be a need of improvements that are a bit more aggressive. Or perhaps expanding the research spectrum to ideas that may be a bit riskier.

Possibly on of the most important parts of building an effective Artificial Intelligence is to feed it information from diverse data sources. Through exposure to labelled images, AI software can be gradually taught to distinguish between objects. This technique is called'supervised learning', as the algorithm is spoon fed readily categorised information. The thing is, the vast majority of data isn't labelled. This means that supervised learning is limited – and so are the algorithms that use it.