Anomaly detection is of great significance for intelligent surveillance videos. Current works typically struggle with object detection and localization problems due to crowded and complex scenes. Hence, we propose a Deep Spatiotemporal Translation Network (DSTN), novel unsupervised anomaly detection and localization method based on Generative Adversarial Network (GAN) and Edge Wrapping (EW). In training, we use only the frames of normal events in order to generate their corresponding dense optical flow as temporal features. During testing, since all the video sequences are input into the system, unknown events are considered as anomalous events due to the fact that the model knows only the normal patterns. To benefit from the information provided by both appearance and motion features, we introduce (i) a novel fusion of background removal and real optical flow frames with (ii) a concatenation of the original and background removal frames. We improve the performance of anomaly localization in the pixel-level evaluation by proposing (iii) the Edge Wrapping to reduce the noise and suppress non-related edges of abnormal objects. Our DSTN has been tested on publicly available anomaly datasets, including UCSD pedestrian, UMN, and CUHK Avenue. The results show that it outperforms other state-of-the-art algorithms with respect to the frame-level evaluation, the pixel-level evaluation, and the time complexity for abnormal object detection and localization tasks.

Fully supervised medical image segmentation still relies on labor-intensive, pixel-level annotations, which limits scale across cohorts and imaging settings. Scribble supervision reduces this burden, yet many CNN-based methods struggle under sparse labels due to weak global context and poor boundary handling. We address these issues with SGGAB-GAN, a scribble-supervised framework that uses adversarial learning, residual attention, and an enhanced feature pipeline built upon two modules: the Superpixel-Guided Graph-Attention Boundary (SGGAB) block and the Adaptive Feature Refinement Block (AFRB). First, the SGGAB block propagates limited scribble cues over a superpixel graph and reinjects boundary information, yielding crisp edges even with few annotations. Second, the AFRB fuses global context with local detail and works with residual attention gates to focus on anatomically relevant regions. On ACDC and MSCMRseg, SGGAB-GAN attains average Dice scores of 0.902 and 0.871, respectively, outperforming scribble-based methods such as ScribFormer and CycleMix while narrowing the gap to full supervision to under 2%. These results indicate that SGGAB-GAN provides high-quality segmentation at a fraction of the labeling cost, making it a scalable choice.

Magnetic Resonance Imaging (MRI) is a critical imaging technique that provides detailed visualization of internal structures without harmful radiation. This review focuses on key MRI modalities, including T1-weighted and T2-weighted imaging, functional MRI (fMRI), and diffusion MRI (dMRI). T1-weighted images offer precise anatomical details, whereas T2-weighted images are essential for highlighting abnormalities such as tumors and inflammation. Functional MRI (fMRI) captures blood flow changes related to neural activity, and diffusion MRI (dMRI) tracks the movement of water molecules within brain tissues. Our review synthesizes insights from 173 studies across major databases, including PubMed, ACM Digital Library, IEEE Xplore, and Google Scholar. We emphasize the versatility of transformer architectures in neuroimaging applications, such as segmentation, detection, reconstruction, and super-resolution, with a particular focus on brain tumor segmentation as a notable achievement. Despite these successes, there remains a significant gap in research, highlighting the need for further studies and collaborative efforts to fully realize the potential of transformers in brain MRI applications. Following the PRISMA-ScR guidelines, this analysis explores current trends, dataset availability, and the overall research landscape. It calls on the scientific community to investigate the underexplored capabilities of transformers, aiming to inspire comprehensive research that could revolutionize brain MRI analysis and advance the fields of medical imaging and neuroscience.

Recent advances in image super-resolution have investigated various transformer and CNN techniques to improve quantitative and perceptual outcomes. Reconstructing high-resolution images from their low-resolution equivalents by combining the power of transformers and CNN has been a crucial task in recent times.We propose a novel U-shaped architecture that integrates transformers and convolutional neural networks (CNNs) to leverage the strengths of both approaches. The network incorporates a novel Parallel Hybrid Transformer CNN Block (PHTCB) on the backbone of the U-shaped design, ensuring computational efficiency and robust hierarchical feature representation. Our architecture incorporates triple-enhanced spatial-attention mechanisms and a Transformer CNN (TCN) Block in PHTCB. The TCN Block helps preserve sharp edges and intricate details often lost in traditional SISR methods and enhances the visual fidelity of the reconstructed high-resolution images. Additionally, we introduce the triple-enhanced spatial attention (TESA) approach that helps precisely localize of important features. Blurring can be reduced for crucial features by focusing on these critical areas because of the network’s ability to control features at various scales. Experiments demonstrate that our proposed method yields better quantitative measurements, including visually appealing high-resolution image reconstructions, peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM).

The current advancements in image super-resolution have explored different attention mechanisms to achieve better quantitative and perceptual results. The critical challenge recently is to utilize the potential of attention mechanisms to reconstruct high-resolution images from their low-resolution counterparts. This research proposes a novel method that combines inception blocks, non-local sparse attention, and a U-Net network architecture. The network incorporates the non-local sparse attention on the backbone of symmetric encoder-decoder U-Net structure, which helps to identify long-range dependencies and exploits contextual information while preserving global context. By incorporating skip connections, the network can leverage features at different scales, enhancing the reconstruction of high-frequency information. Additionally, we introduce inception blocks allowing the model to capture information at various levels of abstraction to enhance multi-scale representation learning further. Experimental findings show that our suggested approach produces superior quantitative measurements, such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), visual information fidelity (VIF), and visually appealing high-resolution image reconstructions.

Integrating Stereo Imaging technology into medical diagnostics and surgeries marks a significant revolution in medical sciences. This advancement gives surgeons and physicians a deeper understanding of patients’ organ anatomy. However, like any technology, stereo cameras have their limitations, such as low resolution (LR) and output images that are often blurry. Our paper introduces a novel approach—a multi-stage network with a pioneering Stereo Endoscopic Attention Module (SEAM). This network aims to progressively enhance the quality of super-resolution (SR), moving from coarse to fine details. Specifically, we propose an edge-guided stereo attention mechanism integrated into each interaction of stereo features. This mechanism aims to capture consistent structural details across different views more effectively. Our proposed model demonstrates superior super-resolution reconstruction performance through comprehensive quantitative evaluations and experiments conducted on three datasets. Our E-SEVSR framework demonstrates superiority over alternative approaches. This framework leverages the edge-guided stereo attention mechanism within the multi-stage network, improving super-resolution quality in medical imaging applications.