Breast cancer remains a leading cause of mortality among women, underlining the need for more effective detection techniques. Recently, AI technologies such as computer vision, machine learning, and deep learning have become increasingly popular in medical imaging and have significantly advanced early cancer detection, enhancing patient outcomes and survival rates. This study presents a novel Hybrid deep learning model EfficientNetV2 and Vision Transformer (EffNetV2+ViT) for identifying breast cancer. The hybrid model is a combination of EfficientNetV2, which is ideal for initially extracting key features from the data and passing those features to the vision transformer, which is effective in data classification. We employed this model on the “BreakHis” dataset. As in this study, three different combinations of the EfficientNetV2 (small, medium, and large) are used, in order to extract the key features from the dataset and optimize the feature extraction process. These features are then fed to the vision transformer, to determine the presence of breast cancer. The models were trained and evaluated for binary and multi-class classification using different magnification levels of BrakHis such as 40x,100x,200x and 400x. Our experiments demonstrate promising results, particularly when using the “large” version of the EfficientNetV2 with the vision transformer, which is successful in detecting breast cancer, achieving an accuracy of 99.83% in binary classifications (benign, malignant) and 98.10% in multi-class scenario (fibro adenoma, ad enosis, tubular adenoma, phyllodes tumour, mucinous carcinoma, papillary carcinoma, lobular carcinoma, ductal carcinoma). These findings suggest that integrating these two approaches could significantly enhance the accuracy of breast cancer detection.

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.

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.

The adoption of Stereo Imaging technology within endoscopic procedures represents a transformative advancement in medical imaging, providing surgeons with depth perception and detailed views of internal anatomy for enhanced diagnostic accuracy and surgical precision. However, the practical application of stereo imaging in endoscopy faces challenges, including the generation of low-resolution and blurred images, which can hinder the effectiveness of medical diagnoses and interventions. Our research introduces an endoscopic image SR model in response to these specific. This model features an innovative feature extraction module and an advanced cross-view feature interaction module tailored for the intricacies of endoscopic imagery. Initially trained on the SCARED dataset, our model was rigorously tested across four additional publicly available endoscopic image datasets at scales 2, 4, and 8, demonstrating unparalleled performance improvements in endoscopic SR. Our results are compelling. They show that our model not only substantially enhances the quality of endoscopic images but also consistently surpasses other existing methods like E-SEVSR [6], DCCSRNet [10], and CCSBESR [5] in all tested datasets, in quantitative measures such as PSNR and SSIM, and in qualitative evaluations. The successful application of our SR model in endoscopic imaging has the potential to revolutionize medical diagnostics and surgery, significantly increasing the precision and effectiveness of endoscopic procedures. The code will be released on GitHub and can be accessed at https://github.com/cu-vtrg-lab/Saliency-Aware-Deep-Learning-Approach-for-Enhanced-Endoscopic-Image-SR.

Super-resolution (SR) techniques are pivotal in enhancing low-resolution images and crucial in medical diagnosis, where detail and clarity are paramount. Traditional pixel-loss-based SR methods, while adept at producing high-resolution (HR) images, often result in artifice content. This loss of information compromises both the visual experience and the accuracy of subsequent diagnoses. Addressing this, we have developed an innovative SR approach integrating a joint Squeeze and Excitation (SE) mechanism with a Combined Channel and Spatial Attention (CCSA) mechanism. The SE mechanism effectively recalibrates channel-wise feature responses, enhancing the representational capacity of the network. Meanwhile, the CCSA mechanism focuses on extracting spatial and channel-wise features, ensuring that critical high-frequency details are preserved. The dual approach significantly refines the quality of the images, maintaining essential details necessary for accurate medical diagnosis. To validate our proposed approach, we used a benchmark dataset, bcSR, tailored to challenge SR models to focus on broader and more critical regions. Comparative analysis proves that our model excels in performance over existing state-of-the-art methods. In conclusion, our proposed SR Network, with its innovative SE and CCSA mechanisms, offers a potent tool for pathology image SR. It elevates the quality of super-resolved images, which will significantly aid in the accuracy and efficiency of medical diagnoses, providing a valuable asset to medical professionals.