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.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely impairs cognitive function and quality of life. Timely intervention in AD relies heavily on early and precise diagnosis, which remains challenging due to the complex and subtle structural changes in the brain. Most existing deep learning methods focus only on a single plane of structural magnetic resonance imaging (sMRI) and struggle to accurately capture the complex and nonlinear relationships among pathological regions of the brain, thus limiting their ability to precisely identify atrophic features. To overcome these limitations, we propose an innovative framework, MPF-KANSC, which integrates multi-plane fusion (MPF) for combining features from the coronal, sagittal, and axial planes, and a Kolmogorov-Arnold Network-guided spatial-channel attention mechanism (KANSC) to more effectively learn and represent sMRI atrophy features. Specifically, the proposed model enables parallel feature extraction from multiple anatomical planes, thus capturing more comprehensive structural information. The KANSC attention mechanism further leverages a more flexible and accurate nonlinear function approximation technique, facilitating precise identification and localization of disease-related abnormalities. Experiments on the ADNI dataset confirm that the proposed MPF-KANSC achieves superior performance in AD diagnosis. Moreover, our findings provide new evidence of right-lateralized asymmetry in subcortical structural changes during AD progression, highlighting the model's promising interpretability.

Machine learning models have utilized semantic features, deep features, or both to assess lung nodule malignancy. However, their reliance on manual annotation during inference, limited interpretability, and sensitivity to imaging variations hinder their application in real-world clinical settings. Thus, this research aims to integrate semantic features derived from radiologists' assessments of nodules, guiding the model to learn clinically relevant, robust, and explainable imaging features for predicting lung cancer. We obtained 938 low-dose CT scans from the National Lung Screening Trial (NLST) with 1,246 nodules and semantic features. Additionally, the Lung Image Database Consortium dataset contains 1,018 CT scans, with 2,625 lesions annotated for nodule characteristics. Three external datasets were obtained from UCLA Health, the LUNGx Challenge, and the Duke Lung Cancer Screening. We fine-tuned a pretrained Contrastive Language-Image Pretraining (CLIP) model with a parameter-efficient fine-tuning approach to align imaging and semantic text features and predict the one-year lung cancer diagnosis. Our model outperformed state-of-the-art (SOTA) models in the NLST test set with an AUROC of 0.901 and AUPRC of 0.776. It also showed robust results in external datasets. Using CLIP, we also obtained predictions on semantic features through zero-shot inference, such as nodule margin (AUROC: 0.812), nodule consistency (0.812), and pleural attachment (0.840). Our approach surpasses the SOTA models in predicting lung cancer across datasets collected from diverse clinical settings, providing explainable outputs, aiding clinicians in comprehending the underlying meaning of model predictions. This approach also prevents the model from learning shortcuts and generalizes across clinical settings. The code is available at https://github.com/luotingzhuang/CLIP_nodule.

The use of diverse modalities, such as omics, medical images, and clinical data can not only improve the performance of prognostic models but also deepen an understanding of disease mechanisms and facilitate the development of novel treatment approaches. However, medical data are complex, often incomplete, and contains missing modalities, making effective handling its crucial for training multimodal models. We introduce impuTMAE, a novel transformer-based end-to-end approach with an efficient multimodal pre-training strategy. It learns inter- and intra-modal interactions while simultaneously imputing missing modalities by reconstructing masked patches. Our model is pre-trained on heterogeneous, incomplete data and fine-tuned for glioma survival prediction using TCGA-GBM/LGG and BraTS datasets, integrating five modalities: genetic (DNAm, RNA-seq), imaging (MRI, WSI), and clinical data. By addressing missing data during pre-training and enabling efficient resource utilization, impuTMAE surpasses prior multimodal approaches, achieving state-of-the-art performance in glioma patient survival prediction. Our code is available at https://github.com/maryjis/mtcp

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.

BackgroundInterstitial lung disease (ILD) encompasses diverse pulmonary disorders with varied prognoses. Current pathological diagnoses suffer from inter-observer variability,necessitating more standardized approaches. We developed an ensemble model AI for cryobiopsy, CRAI, an artificial intelligence model to analyze transbronchial lung cryobiopsy (TBLC) specimens and predict patient outcomes. MethodsWe developed an explainable AI model, CRAI, to analyze TBLC. CRAI comprises seven modules for detecting histological features, generating 19 pathologically significant findings. A downstream XGBoost classifier was developed to predict disease progression using these findings. The models performance was evaluated using respiratory function changes and survival analysis in cross-validation and external test cohorts. FindingsIn the internal cross-validation (135 cases), the model predicted 105 cases without disease progression and 30 with disease progression. The annual {Delta}%FVC was -1.293 in the non-progressive group versus -5.198 in the progressive group, outperforming most pathologists diagnoses. In the external test cohort (48 cases), the model predicted 38 non-progressive and 10 progressive cases. Survival analysis demonstrated significantly shorter survival times in the progressive group (p=0.034). InterpretationCRAI provides a comprehensive, interpretable approach to analyzing TBLC specimens, offering potential for standardizing ILD diagnosis and predicting disease progression. The model could facilitate early identification of progressive cases and guide personalized therapeutic interventions. FundingNew Energy and Industrial Technology Development Organization (NEDO) and Japanese Ministry of Health, Labor, and Welfare.

White matter hyperintensities (WMH) are frequently observed on MRI in aging and Alzheimers disease (AD), yet their microstructural pathology remains poorly characterized. Conventional MRI sequences provide limited information to describe the tissue abnormalities underlying WMH, while histopathology--the gold standard--can only be applied postmortem. Quantitative MRI (qMRI) offers promising non-invasive alternatives to postmortem histopathology, but lacks histological validation of these metrics in AD. In this study, we examined the relationship between MRI metrics and histopathology in postmortem brain scans from eight donors with AD from the South Texas Alzheimers Disease Research Center. Regions of interest are delineated by aligning MRI-identified WMH in the brain donor scans with postmortem histological sections. Histopathological features, including myelin integrity, tissue vacuolation, and gliosis, are quantified within these regions using machine learning. We report the correlations between these histopathological measures and two qMRI metrics: T2 and absolute myelin water signal (aMWS) maps, as well as conventional T1w/T2w MRI. The results derived from aMWS and T2 mapping indicate a strong association between WMH, myelin loss, and increased tissue vacuolation. Bland-Altman analyses indicated that T2 mapping showed more consistent agreement with histopathology, whereas the derived aMWS demonstrated signs of systematic bias. T1w/T2w values exhibited weaker associations with histological alterations. Additionally, we observed distinct patterns of gliosis in periventricular and subcortical WMH. Our study presents one of the first histopathological validations of qMRI in AD, confirming that aMWS and T2 mapping are robust, non-invasive biomarkers that offer promising ways to monitor white matter pathology in neurodegenerative disorders.

The objective of this study is to investigate the predictive ability of abdominal fat features derived from computed tomography (CT) to predict early recurrence within a year following the initial transurethral resection of bladder tumor (TURBT) in patients with non-muscle-invasive bladder cancer (NMIBC). A predictive model is constructed in combination with clinical factors to aid in the evaluation of the risk of early recurrence among patients with NMIBC after initial TURBT. This retrospective study enrolled 325 NMIBC patients from three centers. Machine-learning-based visceral adipose tissue (VAT) radiomics models (VAT-RM) and subcutaneous adipose tissue (SAT) radiomics models (SAT-RM) were constructed to identify patients with early recurrence. A combined model integrating VAT-RM and clinical factors was established. The predictive performance of each variable and model was analyzed using the area under the receiver operating characteristic curve (AUC). The net benefit of each variable and model was presented through decision curve analysis (DCA). The calibration was evaluated utilizing the Hosmer-Lemeshow test. The VAT-RM demonstrated satisfactory performance in the training cohort (AUC = 0.853, 95% CI 0.768-0.937), test cohort 1 (AUC = 0.823, 95% CI 0.730-0.916), and test cohort 2 (AUC = 0.808, 95% CI 0.681-0.935). Across all cohorts, the AUC values of the VAT-RM were higher than those of the SAT-RM (P < 0.001). The DCA curves further confirmed that the clinical net profit of the VAT-RM was superior to that of the SAT-RM. In multivariate logistic regression analysis, the VAT-RM emerged as the most significant independent predictor (odds ratio [OR] = 0.295, 95% CI 0.141-0.508, P < 0.001). The fusion model exhibited excellent AUC values of 0.938, 0.909, and 0.905 across three cohorts. The fusion model surpassed the traditional risk assessment frameworks in both predictive efficacy and clinical net benefit. VAT serves as a crucial factor in early postoperative recurrence in NMIBC patients. The VAT-RM can accurately identify high-risk patients with early postoperative recurrence, offering significant advantages over SAT-RM. The new predictive model constructed by integrating the VAT-RM and clinical factors exhibits excellent predictive performance, clinical net benefits, and calibration accuracy.

Alzheimer's disease (AD) is the most common form of dementia, and it is important to diagnose the disease at an early stage to help people with the condition and their families. Recently, artificial intelligence, especially deep learning approaches applied to medical imaging, has shown potential in enhancing AD diagnosis. This comprehensive review investigates the current state of the art in multimodal deep learning for the early diagnosis of Alzheimer's disease using image processing. The research underpinning this review spanned several months. Numerous deep learning architectures are examined, including CNNs, transfer learning methods, and combined models that use different imaging modalities, such as structural MRI, functional MRI, and amyloid PET. The latest work on explainable AI (XAI) is also reviewed to improve the understandability of the models and identify the particular regions of the brain related to AD pathology. The results indicate that multimodal approaches generally outperform single-modality methods, and three-dimensional (volumetric) data provides a better form of representation compared to two-dimensional images. Current challenges are also discussed, including insufficient and/or poorly prepared datasets, computational expense, and the lack of integration with clinical practice. The findings highlight the potential of applying deep learning approaches for early AD diagnosis and for directing future research pathways. The integration of multimodal imaging with deep learning techniques presents an exciting direction for developing improved AD diagnostic tools. However, significant challenges remain in achieving accurate, reliable, and understandable clinical applications.