Recent advancements in Weakly Supervised Semantic Segmentation (WSSS) have highlighted the use of image-level class labels as a form of supervision. Many methods use pseudo-labels from class activation maps (CAMs) to address the limited spatial information in class labels. However, CAMs generated from Convolutional Neural Networks (CNNs) are often led to focus on prominent features, making it difficult to distinguish foreground objects from their backgrounds. While recent studies show that features from Vision Transformers (ViTs) are more effective in capturing the scene layout than CNNs, the use of hierarchical ViTs has not been widely studied in WSSS. This work introduces "SWTformer" and explores the effect of Swin Transformer’s local-to-global view on improving the accuracy of initial seed CAMs. SWTformer-V1 produces CAMs solely based on patch tokens as its input features. SWTformer-V2 enhances this process by integrating a multi-scale feature fusion mechanism and employing a background-aware mechanism that refines the accuracy of localization maps, resulting in better differentiation between objects. Experiments on the Pascal VOC 2012 dataset demonstrate that compared to state-of-the-art models, SWTformer-V1 achieves 0.98% mAP higher in localization accuracy and generates initial localization maps that are 0.82% mIoU higher in accuracy while relying solely on the classification network. SWTformer-V2 enhances the accuracy of the seed CAMs by 5.32% mIoU. Code available at: ttps://github.com/RozhanAhmadi/SWTformer

Background: Breast cancer is one of the most common cancers in women. Mammogram images have an important role in the treatment of various states of this cancer. In recent years, machine learning methods have been widely used for tumor Segmentation in mammogram images. Pixelbased Segmentation methods have been presented using both Supervised and unSupervised learning approaches. Supervised learning methods are usually fast and accurate, but they usually use a large number of labeled data. Besides, providing these samples is very hard and usually expensive. UnSupervised learning methods do not require the labels of the training data for decision making and they completely ignore the prior knowledge that may lead to a low performance. Semi-Supervised learning methods which use a small number of labeled data solve the problem of providing the high number of samples in Supervised methods, while they usually result in a higher accuracy in comparison to the unSupervised methods. Methods: In this study, we used a semiSupervised method for tumor Segmentation in which the pixel information is used for the classification. The static and gray level run length matrix features for each pixel are considered as the features, and Fisher discriminant analysis (FDA) is used for feature reduction. A cotraining algorithm based on support vector machine and Bayes classifiers is proposed for tumor Segmentation on MIAS data set. Results and Conclusion: The results show that the proposed method outperforms both Supervised methods.

Brain MR images tissue Segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image Segmentation with two Supervised and unSupervised approaches up to now. Supervised Segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unSupervised Segmentation methods have no prior knowledge and lead to low level of performance. However, semi-Supervised learning (SSL) which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-Supervised framework for segmenting of brain MRIs tissues that it has been used results of several semi-Supervised classifiers simultaneously. Selecting appropriate classifiers has an important role in the performance of this framework. Hence, in this paper we present two semi-Supervised algorithms EFM and MCo_Training that are improved versions of semi-Supervised methods EM and Co_Training and increase Segmentation accuracy. Afterwards, we use these improved classifiers together with Graph-Based semi-Supervised classifier as components of the ensemble framework. Experimental results show that performance of Segmentation in this approach is higher than both Supervised methods and the individual semi-Supervised classifiers.