13 accepted papers
Recent studies have demonstrated the effectiveness of modularly integrating traditional machine learning methods, such as Support Vector Machines (SVMs), into neural networks for end-to-end optimization. However, current approaches mostly rely on static embedding, failing to leverage SVM's geometric…
Medical vision-language pre-training (VLP) offers significant potential for advancing medical image understanding by leveraging paired image-report data. However, existing methods are limited by False Negatives (FaNe) induced by semantically similar texts and insufficient fine-grained cross-modal al
Existing graph models predominantly utilize self-attention mechanisms to model feature correlations between the joints of each sample, which not only neglects dynamic relation dependencies in temporal dimension but also leads to redundant computation and difficulty in establishing a unified framewor
Existing deep learning models have demonstrated potential in Diabetic retinopathy (DR) diagnosis, but they still suffer from three key challenges: reliance on single-source inputs, opaque and untraceable reasoning processes, and the absence of a mechanism for result verification. Thus, we propose a …
Kernel methods are powerful tools for nonlinear learning with well-established theory. The scalability issue has been their long-standing challenge. Despite the existing success, there are two limitations in large-scale kernel methods: (i) The memory overhead is too high for users to afford;…
Diabetic retinopathy (DR), with its large patient population, has become a formidable threat to human visual health. In the clinical diagnosis of DR, multi-view fundus images are considered to be more suitable for DR diagnosis because of the wide coverage of the field of view. Therefore, different f…
In order to obtain good medical image segmentation results, existing studies usually extract multi-scale features or design special attention to obtain global and local information of images. However, the above methods are very cumbersome or have a high computational burden. Theoretically, global an…
Recently, contrastive learning has shown great potential in medical image segmentation. Due to the lack of expert annotations, however, it is challenging to apply contrastive learning in semi-supervised scenes. To solve this problem, we propose a novel uncertainty-guided pixel contrastive learning m…
Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wa…
Recently, a series of deep supervised hashing methods were proposed for binary code learning. However, due to the high computation cost and the limited hardware's memory, these methods will first select a subset from the training set, and then form a mini-batch data to update the network in each ite…
Due to the superiority in exploiting the ubiquitous "spatial-shifting" property in modern multi-way data, the recently proposed low-tubal-rank model has been successfully applied for tensor recovery in signal processing and computer vision. In this paper, we define the generalized tensor Dantzig sel…
Tensor completion arouses much attention in signal processing and machine learning. The tensor train (TT) decomposition has shown better performances than the Tucker decomposition in image and video inpainting. In this paper, we propose a novel tensor completion model based on a newly defined latent…
Real multi-way data may suffer from missing entries, noise and outliers simultaneously. The recently proposed tubal nuclear norm (TNN) has shown its superiority in tensor completion. However, statistical analysis of TNN based models is still deficient. This paper aims to robustly recover a polluted…