International Journal of Imaging Systems and Technology最新文献

The Mamba model performs excellently in natural image processing but faces limitations in analyzing whole slide images (WSIs) for cancer prediction and subtype classification in digital pathology—pathological images feature highly irregular lesion spatial distributions (especially complex small-lesion associations), while Mamba's inherent unidirectional/limited-direction scanning cannot effectively model such multi-dimensional spatial dependencies, failing to capture key pathological structural features. To address this, we propose HCSMIL, a Mamba-based optimized framework tailored to pathological image clinical analysis. It comprehensively captures local lesion spatial topology via multi-directional contextual modeling and integrates a multi-scale pyramid structure to extract global lesion distribution features, jointly enhancing diagnostic accuracy. Validation on authoritative datasets (Camelyon16, TCGA-LUNG, TCGA-Kidney) shows HCSMIL significantly outperforms existing mainstream methods: on TCGA-LUNG, accuracy (ACC), F1 score, and AUC are 0.66%, 1.42%, and 1.25% higher than the second-best method; on TCGA-Kidney, these metrics increase by 1.47%, 0.09%, and 1.00%; on Camelyon16, ACC is 0.77% higher. Notably, HCSMIL achieves an 84% small-lesion recognition rate, substantially exceeding TransMIL (70.59%) and MambaMIL (64.71%), fully demonstrating its strength in capturing complexly distributed lesions and providing reliable technical support for cancer diagnosis.

Lung tumor segmentation using machine learning and artificial intelligence techniques leverages the diagnosis precision through accurate localization of the infections. The prominent factors are the features that reflect the infected region concealed through patterns, boundaries, and edges. In this article, a novel Feature-Derivative Pixel Segmentation (FDPS) method is introduced to improve the tumor segmentation accuracy influenced by the disparity pixel distribution problem. This proposed method is assisted by tuneable recurrent learning (TRL) to vary the feature derivative count for varying segments. The learning inputs are modifiable using different extracted feature derivatives under parity and disparity pixel distributions. By identifying the maximum disparity pixels, the tuneable inputs for the recurrent learning are decided. The computation layer of the learning process identifies the maximum related regions identified under parity and disparity features. Such regions are segmented from multiple pixel distribution points until the image size. This process is therefore iterated to identify maximum conjoined features under different infected regions. The proposed method improves the accuracy by 9.63%, the true positive rate by 10.85%, and reduces the classification error by 10.06% for the maximum regions.

Diabetic retinopathy (DR) is a leading cause of blindness among individuals with diabetes. Timely diagnosis and precise classification of DR are essential for patients. However, the traditional diagnostic methods have limitations in precision, mainly relying on doctors' experiences and subjective judgments on DR images. Therefore, an efficient network model, named SDCSCF-Net, is proposed based on deep learning for DR diagnosis and classification. Firstly, the SE_Double_Conv (SDC) module is designed by integrating the Squeeze-and-Excitation (SE) attention mechanism into the first Double_Conv block of the encoder structure of U-Net to enhance feature representation and suppress redundant information. Secondly, a novel attention mechanism, spatial channel fusion attention (SCFA) module, is proposed to enhance the model's focus on lesion areas and the relationship between the channels, making the model more effectively distinguish subtle differences between adjacent DR classes. Finally, the proposed model is evaluated on the APTOS 2019 dataset, which contains 3662 fundus images. The results show that the proposed model demonstrates superior classification performance for DR compared to other existing approaches, and its accuracy, precision, recall, and F1-score for binary classification of DR are 99.18%, 99.47%, 98.98%, and 99.19%, respectively. For the five-class classification task, the model achieves an accuracy of 84.72%, a precision of 84.12%, a recall of 84.72%, and an F1-score of 84.02%. All the evaluation metrics are obtained from the testing phase of the model. In addition, the Grad-CAM technology is utilized to visualize the key lesion areas concerned by the model and further verifies the effectiveness of the proposed model. It is beneficial to promote the research and practical application in the intelligent diagnosis of DR.

Due to the significant increase in breast cancer cases and the limitations of existing early-stage detection techniques, microwave imaging has emerged as a critical tool for the diagnosis of carcinogenic and malignant cells in various tissues. In this work, a 1 × 8 miniaturized metamaterial-based antenna array is conceived and developed for ultrawideband microwave imaging for early breast cancer diagnosis because of its improved accuracy. The developed antenna array features a small dimension, a wide frequency band, a high gain, and broadside radiation properties. To achieve the wider bandwidth from 3.34 to 6.79 GHz, H-shaped unit cells and T-shaped feed network dimensions are optimized. The obtained wide bandwidth supports the generation of high-quality images. A partial ground plane structure is used to improve impedance matching and further enhance the bandwidth. Antenna performance is first numerically and experimentally validated in free space. The antenna performance is validated via numerical simulation and experimental measurements in free space. A numerical phantom with similar tissue properties is created with and without the tumor. Differences in the back scattered signals from the antenna array elements can be observed due to the higher water content and larger dielectric constant of malignant cells as compared to healthy ones, which can be analyzed to identify the tumor or cancer. Here, eight antenna elements are arranged in a circle at a distance of 10 mm from the breast. The separation between adjacent antenna elements is 17 mm to reduce the mutual coupling. Furthermore, the breast tissue is scanned at different angles. At a time, one antenna is excited and the others are in the receiving mode. The collected signals are used to detect malignant cells. The existence of a tumor causes differences in the back scattered signals of the antenna elements. The absolute difference in transmission coefficients, with and without the presence of a tumor, is used to detect the existence of malignant cells. The suggested structure has demonstrated effective performance in microwave imaging using S-parameter contrast.

Cervical cancer is one of the most common malignant tumors among women worldwide, and accurate early diagnosis is critical for improving patient survival rates. Traditional cytological screening methods rely on manual microscopic examination, which suffers from low efficiency and high subjectivity. In recent years, deep learning has facilitated the automation of cervical cell image analysis, yet challenges such as insufficient modeling of pathological features and high computational cost remain. To address these issues, this study proposes a novel dual-branch multi-scale model, MorphoFormer. The model employs a multi-scale dilated Transformer (DilateFormer) as its backbone and innovatively incorporates specialized modules for each branch: A Local Context Aggregation (LCA) module in the local branch and a Global Focus Attention (GFA) module in the global branch. These modules respectively enhance the representation of local details and global semantics, and their features are fused to enable collaborative multi-scale information modeling. Experimental results on the publicly available SIPaKMeD dataset demonstrate that MorphoFormer achieves classification accuracies of 99.58%, 98.51%, and 98.14% for binary, three-class, and five-class tasks, respectively. Further validation on the Blood Cell Count and Detection (BCCD) dataset indicates strong cross-task robustness. Moreover, MorphoFormer requires only 8.22 GFLOPs for inference, highlighting its practical potential by achieving high performance with low computational overhead. Related codes: https://github.com/sijhb/MorphoFormer.

Traditional unsupervised domain adaptation methods usually depend on source domain data distribution for cross-domain alignment. However, direct access to source data is often restricted due to privacy concerns and intellectual property rights. Without using source data, Source-free unsupervised domain adaptation methods can align the pre-trained model with the target domain by generating pseudo-labels for target domain data, which are then used as labeled samples to guide transfer learning. However, methods that generate pseudo-labels solely through iterative averaging often neglect spatial correlations among pixels and are susceptible to noise, resulting in blurred label boundaries. To this end, we propose a source-free domain adaptation framework for fundus image segmentation, which consists of a Multiscale Feature Fusion module for generating high-quality pseudo-labels and a Stepwise Attention Integration module for enhancing model training. The Multiscale Feature Fusion module refines the initial pseudo-labels from the pre-trained model through neighborhood value filling, effectively reducing noise and sharpening label boundaries. The Stepwise Attention Integration module progressively integrates high-level and low-level feature information into the low-level representation. The fused features preserve high-resolution details and enrich semantic content, thereby substantially enhancing the model's recognition capability. Experimental results demonstrate that, without using any source domain images or modifying the pre-trained model, our method achieves performance comparable to or even surpassing state-of-the-art approaches.

Retinopathy, a prevalent retinal disorder, poses a major risk of vision loss if not detected at an early stage. Automatic lesion segmentation plays a key role in effective diagnosis and disease monitoring. In this work, we present a complete and interpretable pipeline that combines YOLOv12 for lesion segmentation, SVD-CAM for visual explanation, and transformer-based Gradient Boosted Neural Networks (GBNN) and Random Forest classifiers for diabetic retinopathy (DR) severity grading. YOLOv12 (You Only Look Once, version 12), known for its real-time object detection capability, is adapted to the complex task of retinal lesion segmentation, delivering both high accuracy and speed. To enhance lesion localization, SVD-CAM generates precise heatmaps that highlight critical pathological regions influencing the grading decision. The segmented lesions are then quantified and used as input features for the grading stage, enabling clinically aligned DR classification. Our approach not only achieves state-of-the-art performance across three public datasets (IDRiD, DDR, and FGADR) but also provides lesion-level interpretability that improves clinical trust and adoption. Extensive experiments demonstrate that the proposed framework delivers accurate segmentation, reliable grading, and meaningful visual explanations, establishing a robust solution for automated DR analysis.

The rising prevalence of lumbar spine disorders demands scalable solutions for mass screening and automated diagnosis. Accurate analysis of specific MRI slices, such as mid-sagittal or transverse mid-height intervertebral disc (IVD) slices, is essential but currently relies on time-consuming, error-prone manual selection. Automating this process is crucial to enhance the efficiency and accuracy of computer-aided diagnostic systems. To address this need, this study introduces a novel deep learning-based framework—SpineDeep-Net that integrates self-attention mechanisms within a multi-layer convolutional neural network for automatic selection of optimal transverse planes of lumbar spine MRI disc slices. By focusing on mid-height slices of L3/L4, L4/L5, and L5/S1 IVDs—the most diagnostically relevant slices, SpineDeep-Net eliminates the reliance on manual selection processes, thereby accelerating and improving the diagnostic pipeline. Unlike standard attention, the proposed dual-self-attention employs two sequential attention stages that jointly enhance long-range spatial cue extraction and emphasize subtle disc-level differences. This mechanism enables the model to focus more effectively on diagnostically relevant regions within lumbar MRI slices by dynamically recalibrating feature maps and strengthening feature dependencies. Experimental evaluations demonstrate the superior performance of SpineDeep-Net, achieving 96.83% accuracy and 98.41% specificity, outperforming state-of-the-art methods. By automating the selection and classification of clinically critical disc slices, SpineDeep-Net addresses a key challenge in lumbar spine diagnostics, providing a reliable, scalable, and efficient tool that aids radiologists in making informed clinical decisions. The proposed framework highlights the transformative potential of self-attention-guided deep learning in advancing healthcare diagnostics. The source code is publicly available at https://github.com/rakeshchandrajoshi/spinedeepnet.