Diabetic Macular Edema (DME), a prevalent complication among diabetic patients, constitutes a major cause of visual impairment and blindness. Although deep learning has achieved remarkable progress in medical image analysis, traditional DME diagnosis still relies on extensive annotated data and subjective ophthalmologist assessments, limiting practical applications. To address this, we present RURANET++, an unsupervised learning-based automated DME diagnostic system. This framework incorporates an optimized U-Net architecture with embedded Spatial and Channel Squeeze & Excitation (SCSE) attention mechanisms to enhance lesion feature extraction. During feature processing, a pre-trained GoogLeNet model extracts deep features from retinal images, followed by PCA-based dimensionality reduction to 50 dimensions for computational efficiency. Notably, we introduce a novel clustering algorithm employing multi-projection heads to explicitly control cluster diversity while dynamically adjusting similarity thresholds, thereby optimizing intra-class consistency and inter-class discrimination. Experimental results demonstrate superior performance across multiple metrics, achieving maximum accuracy (0.8411), precision (0.8593), recall (0.8411), and F1-score (0.8390), with exceptional clustering quality. This work provides an efficient unsupervised solution for DME diagnosis with significant clinical implications.

Specifically: (1) The author mentioned some information-related graph representation works, such as Deep Graph Infomax, InfoGraph, etc, which seems to have similar intuition and technical implementation with this work. However, this paper only briefly discuss that these works are for unsupervised learning, while this paper focused on robust supervised training. This is not enough to clearly state the contribution of this work's approach. Better to elaborate more on the technical difference. GPT-GNN: Generative Pre-Training of Graph Neural Networks (https://arxiv.org/abs/2006.15437), it would be better that the authors can consider adding them as comparison baselines, as the proposed approach and these papers all leverage additional signal to regularize training. Update: Overall, the authors' rebuttal solve many of my concerns, and I do think it has enough novelty and contribution in this field, so I decide to raise up my score.

Abstract: The wave equation is an important physical partial differential equation, and in recent years, deep learning has shown promise in accelerating or replacing traditional numerical methods for solving it. However, existing deep learning methods suffer from high data acquisition costs, low training efficiency, and insufficient generalization capability for boundary conditions. To address these issues, this paper proposes an unsupervised learning method for the wave equation based on finite difference residual constraints. We construct a novel finite difference residual constraint based on structured grids and finite difference methods, as well as an unsupervised training strategy, enabling convolutional neural networks to train without data and predict the forward propagation process of waves. Experimental results show that finite difference residual constraints have advantages over physics-informed neural networks (PINNs) type physical information constraints, such as easier fitting, lower computational costs, and stronger source term generalization capability, making our method more efficient in training and potent in application. Berenger, A perfectly matched layer for the absorption of electromagnetic waves, Journal of computational physics, 卷 114, 期 2, 页 185-200, 1994.

In recent years, deep learning has made significant progress in various computer vision tasks, including image classification, object detection, and segmentation. However, these models often require a large amount of labeled data for training, which can be expensive and time-consuming to collect. To address this issue, unsupervised learning methods have gained attention, particularly contrastive learning. In this article, we will discuss the concept of contrastive learning, how it works, and its applications. Deep learning models require a large amount of labeled data for training.

In the past few weeks, I've been doing research on Linear Regression in Data Science. This week, however, I wanted to change things up. We know a little bit about supervised learning methods and unsupervised learning methods, but we haven't spoken about a different type of learning: Reinforcement Learning. This is the type of learning that would require no supervision, like unsupervised learning, but has unique qualities as well. Before we dive in, one quick note is that Reinforcement Learning is not as widely used as other models, such as Supervised Learning Methods.

As the world enters a fully digital age, cyber threats are on the rise with massive data breaches, hacks into personal and financial data, and any other digital source that people can exploit. To combat these attacks, security experts are increasingly tapping into AI to stay a step ahead using every tool in their toolbox including unsupervised learning methods. Machine learning in the cybersecurity space is considered to still be in its infancy stage, but there has been a lot of traction since 2020 to have more AI involved in the process of combating cyber threats. Understanding how machine learning can be used in cyber security, recognizing the need for unsupervised learning methods in cyber security, and knowing how to implement AI in combating cyber attacks are the key to fighting cybercrime in the years ahead. The scary thing about cybercrime is that it can take up to six months to even detect a breach, and it takes an average of roughly 50 days from the time a breach is found to the time it is reported.

Natural Language Processing is the popular field of Artificial Intelligence. We go to process human language as text or speech to make computers alike humans in this process. Humans have a big amount of data written in a much careless format. That is a problem for any machine to find meaning from raw text. We essential to transforming this data into a vector format to make a machine learn from the raw text.

Deep learning implements structured machine learning algorithms by making use of artificial neural networks. These algorithms help the machine to learn by itself and develop the ability to establish new parameters with which help to make and execute decisions. Deep learning is considered to be a subset of machine learning and utilizes multi-layered artificial neural networks to carry out its processes, which enables it to deliver high accuracy in tasks such as speech recognition, object detection, language translation and other such modern use cases being implemented every day. One of the most intriguing implementations in the domain of artificial intelligence for creating deep learning models has been the Boltzmann Machine. In this article, we will try to understand what exactly a Boltzmann Machine is, how it can be implemented and its uses.

Data science courses contain math--no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits.

It is basically a type of unsupervised learning method . An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labelled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. For ex– The data points in the graph below clustered together can be classified into one single group. We can distinguish the clusters, and we can identify that there are 3 clusters in the below picture. It is not necessary for clusters to be a spherical.