In this paper, we propose a minimum spanning tree-based method for segmenting brain tumors. The proposed method performs interactive segmentation based on the minimum spanning tree without tuning parameters. The steps involve preprocessing, making a graph, constructing a minimum spanning tree, and a newly implemented way of interactively segmenting the region of interest. In the preprocessing step, a Gaussian filter is applied to 2D images to remove the noise. Then, the pixel neighbor graph is weighted by intensity differences and the corresponding minimum spanning tree is constructed. The image is loaded in an interactive window for segmenting the tumor. The region of interest and the background are selected by clicking to split the minimum spanning tree into two trees. One of these trees represents the region of interest and the other represents the background. Finally, the segmentation given by the two trees is visualized. The proposed method was tested by segmenting two different 2D brain T1-weighted magnetic resonance image data sets. The comparison between our results and the gold standard segmentation confirmed the validity of the minimum spanning tree approach. The proposed method is simple to implement and the results indicate that it is accurate and efficient.

Background: Image segmentation is the process of partitioning an image into separate objects or regions. It is an essential step in image processing to segment the regions of interest for further processing. We propose a method for segmenting the nuclei and cytoplasms from white blood cells (WBCs).Methods: Initially, the method computes an initial value based on the minimum and maximum values of the input image. Then, a histogram of the input image is computed and approximated to obtain function values. The method searches for the first local maximum and local minimum from the approximated function values in the order of increasing of knots sequence. We approximate the required threshold from the first local minimum and the computed initial value based on defined conditions. The threshold is applied to the input image to binarize it, and then post-processing is performed to obtain the final segmented nucleus. We segment the whole WBC before segmenting the cytoplasm depending on the complexity of the objects in the image. For WBCs that are well separated from red blood cells (RBCs),nthresholds are generated and then producenthresholded images. Then, a standard Otsu method is used to binarize the average of the produced images. Morphological operations are applied on the binarized image, and then a single-pixel point from the segmented nucleus is used to segment the WBC. For images in which RBCs touch the WBCs, we segment the whole WBC using SLIC and watershed methods. The cytoplasm is obtained by subtracting the segmented nucleus from the segmented WBC.Results: The method is tested on two different public data sets and the results are compared to the state of art methods. The performance analysis shows that the proposed method segments the nucleus and cytoplasm well.Conclusion: We propose a method for nucleus and cytoplasm segmentation based on the local minima of the approximated function values from the image’s histogram. The method has demonstrated its utility in segmenting nuclei, WBCs, and cytoplasm, and the results are satisfactory.

Brain segmentation in magnetic resonance imaging (MRI) images is the process of isolating the brain from non-brain tissues to simplify the further analysis, such as detecting pathology or calculating volumes. This paper proposes a Graph-based Unsupervised Brain Segmentation (GUBS) that processes 3D MRI images and segments them into brain, non-brain tissues, and backgrounds. GUBS first constructs an adjacency graph from a preprocessed MRI image, weights it by the difference between voxel intensities, and computes its minimum spanning tree (MST). It then uses domain knowledge about the different regions of MRIs to sample representative points from the brain, non-brain, and background regions of the MRI image. The adjacency graph nodes corresponding to sampled points in each region are identified and used as the terminal nodes for paths connecting the regions in the MST. GUBS then computes a subgraph of the MST by first removing the longest edge of the path connecting the terminal nodes in the brain and other regions, followed by removing the longest edge of the path connecting non-brain and background regions. This process results in three labeled, connected components, whose labels are used to segment the brain, non-brain tissues, and the background. GUBS was tested by segmenting 3D T1 weighted MRI images from three publicly available data sets. GUBS shows comparable results to the state-of-the-art methods in terms of performance. However, many competing methods rely on having labeled data available for training. Labeling is a time-intensive and costly process, and a big advantage of GUBS is that it does not require labels.

Deep learning model classification on large datasets is often limited in countries with restricted computational resources. While transfer learning can offset these limitations, standard architectures often maintain a high memory footprint. This study introduces HybridNet-XR, a memory-efficient and computationally lightweight hybrid convolutional neural network (CNN) designed to bridge the domain gap in medical radiography using autonomous self-supervised learning protocols. The HybridNet-XR architecture integrates depthwise separable convolutions for parameter reduction, residual connections for gradient stability, and aggressive early downsampling to minimize the video RAM (VRAM) footprint. We evaluated several training paradigms, including teacher-free self-supervised learning (SSL-SimCLR), teacher-led knowledge distillation (KD), and domain-gap (DG) adaptation. Each variant was pre-trained on ImageNet-1k subsets and fine-tuned on the ChestX6 multi-class dataset. Model interpretability was validated through gradient-weighted class activation mapping (Grad-CAM). The performance frontier analysis identified the HybridNet-XR-150-PW (Pre-warmed) as the optimal configuration, achieving a 93.38% average accuracy and 99% AUC while utilizing only 814.80 MB of VRAM. Regarding class-wise accuracy, this variant significantly outperformed standard MobileNetV2 and teacher-led models in critical diagnostic categories, notably Covid-19 (97.98%) and Emphysema (96.80%). Grad-CAM visualizations confirmed that the teacher-free pre-warming phase allows the model to develop sharper, anatomically grounded focus on pathological landmarks compared to distilled models. Specialized pre-warming schedules offer a viable, computationally autonomous alternative to knowledge distillation for medical imaging. By eliminating the requirement for high-performance teacher models, HybridNet-XR provides a robust and trustworthy diagnostic foundation suitable for clinical deployment in resource-constrained environments. Author summary: Traditional deep learning models for medical imaging are often too large for the low-power computers available in many global health settings. We developed a new model to bridge this computational gap. We designed HybridNet-XR, a highly efficient AI architecture, and trained it using a “teacher-free” method that doesn’t require a massive supercomputer. We found a specific version ( H - XR ₁₅₀ - PW ) that provides high accuracy while using very little memory. Our results show that high-performance diagnostic AI can be deployed on standard, low-cost hardware. Furthermore, using visual heatmaps (Grad-CAM), we proved that the AI correctly identifies medical landmarks like lung opacities, ensuring it is safe and reliable for real-world clinical use.

Background: Image segmentation is a process of partitioning the input image into its separate objects or regions. It is an essential step in image processing to segment the regions of interest (ROI) for further processing. We propose a method for segmenting neucleus and cytoplasm from the white blood cells (WBC).Methods: Initially, the method computes an initial value based on the minimum and maximum values of the input image. Then, a histogram of the input image is computed and then approximated to obtain function values. The method searches for the first local maximum and local minimum from the approximated function values. We approximate the required threshold from the first local minimum and the computed initial value based on defined conditions. The threshold is applied to the input image to binarize it and then perform post-processing to obtain the final segmented nucleus. We segment the whole WBC before segmenting the cytoplasm depending on the complexity of the objects in the image. For WBCs which are well separated from the RBCs, n thresholds are generated and then produce n thresholded images. Then, a standard Otsu method is used to binarize the average of the produced images. Morphological operations are applied on the binarized image and then use a single-pixel point from the segmented nucleus to segment the WBCs. For images in which RBCs touch the WBCs, we segment the whole WBC using SLIC and watershed methods. The cytoplasm is obtained by subtracting the segmented nucleus from the segmented WBC. Results: The method is tested on two different public data sets. Performance analysis is done and the results show that the proposed method segments well the nucleus and cytoplasm. Conclusion: We propose a method for nuclei and cytoplasm segmentation based on the local minima of the approximated function values from the histogram of image. The method has demonstrated its utility in segmenting nuclei, WBCs and cytoplasm and the results are reasonable.

Automated triage of whole-slide cervical cytology images remains constrained by the heavy dependence on single-cell bounding boxes or pixel-level annotations, which introduce significant bottlenecks, high expert overhead, and observer variability. To address these challenges, we present a weakly-supervised, detection-free Multiple Instance Learning (MIL) framework powered by a Gated Attention pooling engine that operates exclusively on slide-level predictions. By treating whole-slide images as bags of local instance patches, the dual-branch gated network dynamically assigns non-linear attention weights to highlight isolated dysplastic cells while suppressing benign background and debris, successfully resolving the “needle-in-a-haystack” problem inherent to high-grade lesions. Evaluated across multi-center cohorts including internal datasets (SIPaKMeD, Herlev, and CRIC) and the unannotated, out-of-distribution Mendeley LBC validation cohort processed via an unsupervised marker-controlled watershed pipeline our approach demonstrates robust generalization without requiring dense instance labels. Furthermore, our architectural comparison reveals a key trade-off: lightweight backbones like MobileNetV2 optimize in-distribution multi-center accuracy (90.96% Accuracy, 0.9800 ROC-AUC), whereas higher-capacity models like Xception ensure superior out-of-distribution robustness (80.43% Balanced Accuracy) under domain shifts. Ultimately, this detection-less pipeline provides a scalable and clinically viable solution to automate slide triage and alleviate cytopathologist shortages in resource-constrained environments. Author summary: Automated screening of cervical cytology slides is traditionally hindered by the need for labor-intensive, pixel-level cell annotations. To overcome this limitation, this study introduces a weakly-supervised, detection-free Multiple Instance Learning framework that performs slide triage using only slide-level diagnostic labels. By employing a Gated Attention mechanism, the model dynamically focuses on informative cellular regions while ignoring background debris, successfully addressing the challenge of identifying rare dysplastic cells across gigapixel images. Evaluations across multi-center datasets demonstrate that lightweight models like MobileNetV2 achieve high accuracy on in-distribution data, whereas deeper architectures like Xception provide superior robustness against out-of-distribution variations. Ultimately, this approach offers a scalable, annotation-free triage tool designed to assist cytopathologists and improve screening workflows in resource-constrained environments.