Deep learning has revolutionized medical imaging, but its effectiveness is severely limited by insufficient labeled training data. This paper introduces a novel GAN-based semi-supervised learning framework specifically designed for low labeled-data regimes, evaluated across settings with 5 to 50 labeled samples per class. Our approach integrates three specialized neural networks -- a generator for class-conditioned image translation, a discriminator for authenticity assessment and classification, and a dedicated classifier -- within a three-phase training framework. The method alternates between supervised training on limited labeled data and unsupervised learning that leverages abundant unlabeled images through image-to-image translation rather than generation from noise. We employ ensemble-based pseudo-labeling that combines confidence-weighted predictions from the discriminator and classifier with temporal consistency through exponential moving averaging, enabling reliable label estimation for unlabeled data. Comprehensive evaluation across eleven MedMNIST datasets demonstrates that our approach achieves statistically significant improvements over six state-of-the-art GAN-based semi-supervised methods, with particularly strong performance in the extreme 5-shot setting where the scarcity of labeled data is most challenging. The framework maintains its superiority across all evaluated settings (5, 10, 20, and 50 shots per class). Our approach offers a practical solution for medical imaging applications where annotation costs are prohibitive, enabling robust classification performance even with minimal labeled data. Code is available at https://github.com/GuidoManni/SPARSE.

Lung cancer (LC) ranks among the most frequently diagnosed cancers and is one of the most common causes of death for men and women worldwide. Computed Tomography (CT) images are the most preferred diagnosis method because of their low cost and their faster processing times. Many researchers have proposed various ways of identifying lung cancer using CT images. However, such techniques suffer from significant false positives, leading to low accuracy. The fundamental reason results from employing a small and imbalanced dataset. This paper introduces an innovative approach for LC detection and classification from CT images based on the DenseNet201 model. Our approach comprises several advanced methods such as Focal Loss, data augmentation, and regularization to overcome the imbalanced data issue and overfitting challenge. The findings show the appropriateness of the proposal, attaining a promising performance of 98.95% accuracy.

By carefully analyzing latent image properties, content-based image retrieval (CBIR) systems are able to recover pertinent images without relying on text descriptions, natural language tags, or keywords related to the image. This search procedure makes it quite easy to automatically retrieve images in huge, well-balanced datasets. However, in the medical field, such datasets are usually not available. This study proposed an advanced DL technique to enhance the accuracy of image retrieval in complex medical datasets. The proposed model can be integrated into five stages, namely pre-processing, decomposing the images, feature extraction, dimensionality reduction, and classification with an image retrieval mechanism. The hybridized Wavelet-Hadamard Transform (HWHT) was utilized to obtain both low and high frequency detail for analysis. In order to extract the main characteristics, the Gray Level Co-occurrence Matrix (GLCM) was employed. Furthermore, to minimize feature complexity, Sine chaos based artificial rabbit optimization (SCARO) was utilized. By employing the Bhattacharyya Coefficient for improved similarity matching, the Bhattacharya Context performance aware global attention-based Transformer (BCGAT) improves classification accuracy. The experimental results proved that the COVID-19 Chest X-ray image dataset attained higher accuracy, precision, recall, and F1-Score of 99.5%, 97.1%, 97.1%, and 97.1%, 97.1%, respectively. However, the chest x-ray image (pneumonia) dataset has attained higher accuracy, precision, recall, and F1-score values of 98.60%, 98.49%, 97.40%, and 98.50%, respectively. For the NIH chest X-ray dataset, the accuracy value is 99.67%.

This study aimed to develop and validate a hybrid deep learning (DL) model that integrates convolutional neural network (CNN) and vision transformer (ViT) architectures to predict distant metastasis (DM) in patients with non-small cell lung cancer (NSCLC) using ¹⁸F-FDG PET/CT images. A retrospective analysis was conducted on a cohort of consecutively registered patients who were newly diagnosed and untreated for NSCLC. A total of 167 patients with available PET/CT images were included in the analysis. DL features were extracted using a combination of CNN and ViT architectures, followed by feature selection, model construction, and evaluation of model performance using the receiver operating characteristic (ROC) and the area under the curve (AUC). The ViT-based DL model exhibited strong predictive capabilities in both the training and validation cohorts, achieving AUCs of 0.824 and 0.830 for CT features, and 0.602 and 0.694 for PET features, respectively. Notably, the model that integrated both PET and CT features demonstrated a notable AUC of 0.882 in the validation cohort, outperforming models that utilized either PET or CT features alone. Furthermore, this model outperformed the CNN model (ResNet 50), which achieved an AUC of 0.752 [95% CI 0.613, 0.890], p < 0.05. Decision curve analysis further supported the efficacy of the ViT-based DL model. The ViT-based DL developed in this study demonstrates considerable potential in predicting DM in patients with NSCLC, potentially informing the creation of personalized treatment strategies. Future validation through prospective studies with larger cohorts is necessary.

Neurite Orientation Dispersion and Density Imaging (NODDI) microstructure estimation from diffusion magnetic resonance imaging (dMRI) is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods accelerate the speed of NODDI parameter estimation and improve the accuracy. However, most methods require the number and coordinates of gradient directions during testing and training to remain strictly consistent, significantly limiting the generalization and robustness of these models in NODDI parameter estimation. Therefore, it is imperative to develop methods that can perform robustly under varying diffusion gradient directions. In this paper, we propose a q-space sampling augmentation-based continuous representation learning framework (SamRobNODDI) to achieve robust and generalized NODDI. Specifically, a continuous representation learning method based on q-space sampling augmentation is introduced to fully explore the information between different gradient directions in q- space. Furthermore, we design a sampling consistency loss to constrain the outputs of different sampling schemes, ensuring that the outputs remain as consistent as possible, thereby further enhancing performance and robustness to varying q-space sampling schemes. SamRobNODDI is also a flexible framework that can be applied to different backbone networks. SamRobNODDI was compared against seven state-of-the-art methods across 18 diverse q-space sampling schemes. Extensive experimental validations have been conducted under both identical and diverse sampling schemes for training and testing, as well as across varying sampling rates, different loss functions, and multiple network backbones. Results demonstrate that the proposed SamRobNODDI has better performance, robustness, generalization, and flexibility in the face of varying q-space sampling schemes.&#xD.

Community-acquired pneumonia (CAP) is a significant concern for children worldwide and is associated with a high morbidity and mortality. To improve patient outcomes, early intervention and accurate diagnosis are essential. Artificial intelligence (AI) can mine and label imaging data and thus may contribute to precision research and personalized clinical management. The baseline characteristics of 230 children with severe CAP hospitalized from January 2023 to October 2024 were retrospectively analyzed. The patients were divided into two groups according to the presence of respiratory failure. The predictive ability of AI-derived chest CT (computed tomography) indices alone for respiratory failure was assessed via logistic regression analysis. ROC (receiver operating characteristic) curves were plotted for these regression models. After adjusting for age, white blood cell count, neutrophils, lymphocytes, creatinine, wheezing, and fever > 5 days, a greater number of involved lung lobes [odds ratio 1.347, 95% confidence interval (95% CI) 1.036-1.750, P = 0.026] and bilateral lung involvement (odds ratio 2.734, 95% CI 1.084-6.893, P = 0.033) were significantly associated with respiratory failure. The discriminatory power (as measured by the area under curve) of Model 2 and Model 3, which included electronic health record data and the accuracy of CT imaging features, was better than that of Model 0 and Model 1, which contained only the chest CT parameters. The sensitivity and specificity of Model 2 at the optimal critical value (0.441) were 84.3% and 59.8%, respectively. The sensitivity and specificity of Model 3 at the optimal critical value (0.446) were 68.6% and 76.0%, respectively. The use of AI-derived chest CT indices may achieve high diagnostic accuracy and guide precise interventions for patients with severe CAP. However, clinical, laboratory, and AI-derived chest CT indices should be included to accurately predict and treat severe CAP.

Cardiomyopathy is a life-threatening condition associated with heart failure, arrhythmias, thromboembolism, and sudden cardiac death, posing a significant contribution to worldwide morbidity and mortality. Cardiomegaly, which is usually the initial radiologic sign, may reflect the progression of an underlying heart disease or an underlying undiagnosed cardiac condition. Chest radiography is the most frequently used imaging method for detecting heart enlargement. Prompt and accurate diagnosis is essential for prompt intervention and appropriate treatment planning to prevent disease progression and improve patient outcomes. The current work provides a new methodology for automated cardiomegaly diagnosis using X-ray images through the fusion of Block-Matching and 3D Filtering (BM3D) within the Ensemble Hilbert-Huang Transform (EHHT), convolutional neural networks like Pretrained VGG16, ResNet50, InceptionV3, DenseNet169, and Spiking Neural Networks (SNN), and Classifiers. BM3D is first used for image edge retention and noise reduction, and then EHHT is applied to obtain informative features from X-ray images. The features that have been extracted are then processed using an SNN that simulates neural processes at a biological level and offers a biologically possible classification solution. Gradient-weighted Class Activation Mapping (GradCAM) emphasized important areas that affected model predictions. The SNN performed the best among all the models tested, with 97.6 % accuracy, 96.3 % sensitivity, and 98.2 % specificity. These findings show the SNN's high potential for facilitating accurate and efficient cardiomyopathy diagnosis, leading to enhanced clinical decision-making and patient outcomes.

Breast cancer detection through mammography interpretation remains difficult because of the minimal nature of abnormalities that experts need to identify alongside the variable interpretations between readers. The potential of CNNs for medical image analysis faces two limitations: they fail to process both local information and wide contextual data adequately, and do not provide explainable AI (XAI) operations that doctors need to accept them in clinics. The researcher developed the MammoFormer framework, which unites transformer-based architecture with multi-feature enhancement components and XAI functionalities within one framework. Seven different architectures consisting of CNNs, Vision Transformer, Swin Transformer, and ConvNext were tested alongside four enhancement techniques, including original images, negative transformation, adaptive histogram equalization, and histogram of oriented gradients. The MammoFormer framework addresses critical clinical adoption barriers of AI mammography systems through: (1) systematic optimization of transformer architectures via architecture-specific feature enhancement, achieving up to 13% performance improvement, (2) comprehensive explainable AI integration providing multi-perspective diagnostic interpretability, and (3) a clinically deployable ensemble system combining CNN reliability with transformer global context modeling. The combination of transformer models with suitable feature enhancements enables them to achieve equal or better results than CNN approaches. ViT achieves 98.3% accuracy alongside AHE while Swin Transformer gains a 13.0% advantage through HOG enhancements

Accurate mediastinal tumor classification is crucial for treatment planning, but diagnostic performance varies with radiologists' experience and report quality. To evaluate GPT-4's diagnostic accuracy in classifying mediastinal tumors from radiological reports compared to radiologists of different experience levels using radiological reports of varying quality. We conducted a retrospective study of 1,494 patients from five tertiary hospitals with mediastinal tumors diagnosed via chest CT and pathology. Radiological reports were categorized into low-, medium-, and high-quality based on predefined criteria assessed by experienced radiologists. Six radiologists (two residents, two attending radiologists, and two associate senior radiologists) and GPT-4 evaluated the chest CT reports. Diagnostic performance was analyzed overall, by report quality, and by tumor type using Wald χ2 tests and 95% CIs calculated via the Wilson method. GPT-4 achieved an overall diagnostic accuracy of 73.3% (95% CI: 71.0-75.5), comparable to associate senior radiologists (74.3%, 95% CI: 72.0-76.5; p >0.05). For low-quality reports, GPT-4 outperformed associate senior radiologists (60.8% vs. 51.1%, p<0.001). In high-quality reports, GPT-4 was comparable to attending radiologists (80.6% vs.79.4%, p>0.05). Diagnostic performance varied by tumor type: GPT-4 was comparable to radiology residents for neurogenic tumors (44.9% vs. 50.3%, p>0.05), similar to associate senior radiologists for teratomas (68.1% vs. 65.9%, p>0.05), and superior in diagnosing lymphoma (75.4% vs. 60.4%, p<0.001). GPT-4 demonstrated interpretation accuracy comparable to Associate Senior Radiologists, excelling in low-quality reports and outperforming them in diagnosing lymphoma. These findings underscore GPT-4's potential to enhance diagnostic performance in challenging diagnostic scenarios.

The aim of this study was to develop a radiomic model to non-invasively predict the risk of secondary enucleation (SE) in patients with uveal melanoma (UM) prior to stereotactic radiotherapy using pretreatment computed tomography (CT) and magnetic resonance (MR) images. This retrospective study encompasses a cohort of 308 patients diagnosed with UM who underwent stereotactic radiosurgery (SRS) or fractionated stereotactic radiotherapy (FSRT) using the CyberKnife system (Accuray, Sunnyvale, CA, USA) between 2007 and 2018. Each patient received comprehensive ophthalmologic evaluations, including assessment of visual acuity, anterior segment examination, fundus examination, and ultrasonography. All patients were followed up for a minimum of 5 years. The cohort was composed of 65 patients who underwent SE (SE+) and 243 who did not (SE-). Radiomic features were extracted from pretreatment CT and MR images. To develop a robust predictive model, four different machine learning algorithms were evaluated using these features. The stacking model utilizing CT + MR radiomic features achieved the highest predictive performance, with an area under the curve (AUC) of 0.90, accuracy of 0.86, sensitivity of 0.81, and specificity of 0.90. The feature of robust mean absolute deviation derived from the Laplacian-of-Gaussian-filtered MR images was identified as the most significant predictor, demonstrating a statistically significant difference between SE+ and SE- cases (p = 0.005). Radiomic analysis of pretreatment CT and MR images can non-invasively predict the risk of SE in UM patients undergoing SRS/FSRT. The combined CT + MR radiomic model may inform more personalized therapeutic decisions, thereby reducing unnecessary radiation exposure and potentially improving patient outcomes.