The rapid growth of medical imaging data presents challenges for efficient storage and transmission, particularly in clinical and telemedicine applications where image fidelity is crucial. This study proposes a hybrid deep learning-based image compression framework that integrates Stationary Wavelet Transform (SWT), Stacked Denoising Autoencoder (SDAE), Gray-Level Co-occurrence Matrix (GLCM), and K-means clustering. The framework enables multiresolution decomposition, texture-aware feature extraction, and adaptive region-based compression. A custom loss function that combines Mean Squared Error (MSE) and Structural Similarity Index (SSIM) ensures high perceptual quality and compression efficiency. The proposed model was evaluated across multiple benchmark medical imaging datasets and achieved a Peak Signal-to-Noise Ratio (PSNR) of up to 50.36 dB, MS-SSIM of 0.9999, and an encoding-decoding time of 0.065 s. These results demonstrate the model's capability to outperform existing approaches while maintaining diagnostic integrity, scalability, and speed, making it suitable for real-time and resource-constrained clinical environments.

The application of sophisticated computer vision techniques for medical image segmentation (MIS) plays a vital role in clinical diagnosis and treatment. Although Transformer-based models are effective at capturing global context, they are often ineffective at dealing with local feature dependencies. In order to improve this problem, we design a Multi-scale Fusion and Semantic Enhancement Network (MFSE-Net) for endoscopic image segmentation, which aims to capture global information and enhance detailed information. MFSE-Net uses a dual encoder architecture, with PVTv2 as the primary encoder to capture global features and CNNs as the secondary encoder to capture local details. The main encoder includes the LGDA (Large-kernel Grouped Deformable Attention) module for filtering noise and enhancing the semantic extraction of the four hierarchical features. The auxiliary encoder leverages the MLCF (Multi-Layered Cross-attention Fusion) module to integrate high-level semantic data from the deep CNN with fine spatial details from the shallow layers, enhancing the precision of boundaries and positioning. On the decoder side, we have introduced the PSE (Parallel Semantic Enhancement) module, which embeds the boundary and position information of the secondary encoder into the output characteristics of the backbone network. In the multi-scale decoding process, we also add SAM (Scale Aware Module) to recover global semantic information and offset for the loss of boundary details. Extensive experiments have shown that MFSE-Net overwhelmingly outperforms SOTA on the renal tumor and polyp datasets.

This study investigates a deep learning approach for sex estimation using 3D hyoid bone models derived from computed tomography (CT) scans of a Croatian population. We analyzed 202 hyoid samples (101 male, 101 female), converting CT-derived meshes into 2048-point clouds for processing with an adapted PointNet++ network. The model, optimized for small datasets with 1D convolutional layers and global size features, was first applied in an unsupervised framework. Unsupervised clustering achieved 87.10% accuracy, identifying natural sex-based morphological patterns. Subsequently, supervised classification with a support vector machine yielded an accuracy of 88.71% (Matthews Correlation Coefficient, MCC = 0.7746) on a test set (n = 62). Interpretability analysis highlighted key regions influencing classification, with males exhibiting larger, U-shaped hyoids and females showing smaller, more open structures. Despite the modest sample size, the method effectively captured sex differences, providing a data-efficient and interpretable tool. This flexible approach, combining computational efficiency with practical insights, demonstrates potential for aiding sex estimation in cases with limited skeletal remains and may support broader applications in forensic anthropology.

The early diagnosis of brain tumors is crucial for patient prognosis, and medical imaging techniques such as MRI and CT scans are essential tools for diagnosing brain tumors. However, high-quality medical image data for brain tumors is often scarce and difficult to obtain, which hinders the development and application of medical image analysis models. With the advancement of artificial intelligence, particularly deep learning technologies in the field of medical imaging, new concepts and tools have been introduced for the early diagnosis, treatment planning, and prognosis evaluation of brain tumors. To address the challenge of imbalanced brain tumor datasets, we propose a novel data augmentation technique based on a diffusion model, referred to as the Multi-Channel Fusion Diffusion Model(MCFDiffusion). This method tackles the issue of data imbalance by converting healthy brain MRI images into images containing tumors, thereby enabling deep learning models to achieve better performance and assisting physicians in making more accurate diagnoses and treatment plans. In our experiments, we used a publicly available brain tumor dataset and compared the performance of image classification and segmentation tasks between the original data and the data enhanced by our method. The results show that the enhanced data improved the classification accuracy by approximately 3% and the Dice coefficient for segmentation tasks by 1.5%-2.5%. Our research builds upon previous work involving Denoising Diffusion Implicit Models (DDIMs) for image generation and further enhances the applicability of this model in medical imaging by introducing a multi-channel approach and fusing defective areas with healthy images. Future work will explore the application of this model to various types of medical images and further optimize the model to improve its generalization capabilities. We release our code at https://github.com/feiyueaaa/MCFDiffusion.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health challenge. Despite its rising prevalence, particularly in low and middle-income countries, early diagnosis remains inadequate, with projections estimating over 55 million affected individuals by 2022, expected to triple by 2050. Accurate early detection is critical for effective intervention. This study presents Neuroimaging-based Early Detection of Alzheimer's Disease using Deep Learning (NEDA-DL), a novel computer-aided diagnostic (CAD) framework leveraging a hybrid ResNet-50 and AlexNet architecture optimized with CUDA-based parallel processing. The proposed deep learning model processes MRI and PET neuroimaging data, utilizing depthwise separable convolutions to enhance computational efficiency. Performance evaluation using key metrics including accuracy, sensitivity, specificity, and F1-score demonstrates state-of-the-art classification performance, with the Softmax classifier achieving 99.87% accuracy. Comparative analyses further validate the superiority of NEDA-DL over existing methods. By integrating structural and functional neuroimaging insights, this approach enhances diagnostic precision and supports clinical decision-making in Alzheimer's disease detection.

Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of tumor regions in PBTs from MRI scans. Two pipelines were developed for segmenting enhanced tumor (ET), tumor core (TC), and whole tumor (WT) in pediatric gliomas from the BraTS-PEDs 2024 dataset. First, a pre-trained SegResNet model was retrained with a transfer learning approach and tested on the pediatric cohort. Then, two novel multi-encoder architectures leveraging the attention mechanism were designed and trained from scratch. To enhance the performance on ET regions, an ensemble paradigm and post-processing techniques were implemented. Overall, the 3-encoder model achieved the best performance in terms of Dice Score on TC and WT when trained with Dice Loss and on ET when trained with Generalized Dice Focal Loss. SegResNet showed higher recall on TC and WT, and higher precision on ET. After post-processing, we reached Dice Scores of 0.843, 0.869, 0.757 with the pre-trained model and 0.852, 0.876, 0.764 with the ensemble model for TC, WT and ET, respectively. Both strategies yielded state-of-the-art performances, although the ensemble demonstrated significantly superior results. Segmentation of the ET region was improved after post-processing, which increased test metrics while maintaining the integrity of the data.

This study aims to investigate the diagnostic value of integrating multi-parametric magnetic resonance imaging (mpMRI) radiomic features with tumor abnormal protein (TAP) and clinical characteristics for diagnosing prostate cancer. A cohort of 109 patients who underwent both mpMRI and TAP assessments prior to prostate biopsy were enrolled. Radiomic features were meticulously extracted from T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC) maps. Feature selection was performed using t-tests and the Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by model construction using the random forest algorithm. To further enhance the model's accuracy and predictive performance, this study incorporated clinical factors including age, serum prostate-specific antigen (PSA) levels, and prostate volume. By integrating these clinical indicators with radiomic features, a more comprehensive and precise predictive model was developed. Finally, the model's performance was quantified by calculating accuracy, sensitivity, specificity, precision, recall, F1 score, and the area under the curve (AUC). From mpMRI sequences of T2WI, dADC(b = 100/1000 s/mm²), and dADC(b = 100/2000 s/mm²), 8, 10, and 13 radiomic features were identified as significantly correlated with prostate cancer, respectively. Random forest models constructed based on these three sets of radiomic features achieved AUCs of 0.83, 0.86, and 0.87, respectively. When integrating all three sets of data to formulate a random forest model, an AUC of 0.84 was obtained. Additionally, a random forest model constructed on TAP and clinical characteristics achieved an AUC of 0.85. Notably, combining mpMRI radiomic features with TAP and clinical characteristics, or integrating dADC (b = 100/2000 s/mm²) sequence with TAP and clinical characteristics to construct random forest models, improved the AUCs to 0.91 and 0.92, respectively. The proposed model, which integrates radiomic features, TAP and clinical characteristics using machine learning, demonstrated high predictive efficiency in diagnosing prostate cancer.

To propose a deep learning model and explore its performance in the auxiliary diagnosis of lung cancer associated with cystic airspaces (LCCA) in computed tomography (CT) images. This study is a retrospective analysis that incorporated a total of 342 CT series, comprising 272 series from patients diagnosed with LCCA and 70 series from patients with pulmonary bulla. A deep learning model named LungSSFNet, developed based on nnUnet, was utilized for image recognition and segmentation by experienced thoracic surgeons. The dataset was divided into a training set (245 series), a validation set (62 series), and a test set (35 series). The performance of LungSSFNet was compared with other models such as UNet, M2Snet, TANet, MADGNet, and nnUnet to evaluate its effectiveness in recognizing and segmenting LCCA and pulmonary bulla. LungSSFNet achieved an intersection over union of 81.05% and a Dice similarity coefficient of 75.15% for LCCA, and 93.03% and 92.04% for pulmonary bulla, respectively. These outcomes demonstrate that LungSSFNet outperformed many existing models in segmentation tasks. Additionally, it attained an accuracy of 96.77%, a precision of 100%, and a sensitivity of 96.15%. LungSSFNet, a new deep-learning model, substantially improved the diagnosis of early-stage LCCA and is potentially valuable for auxiliary clinical decision-making. Our LungSSFNet code is available at https://github.com/zx0412/LungSSFNet .

We present the first comparative study of two fundamentally distinct feature extraction techniques: Histogram of Oriented Gradients (HOG) and Topological Data Analysis (TDA), for medical image classification using retinal fundus images. HOG captures local texture and edge patterns through gradient orientation histograms, while TDA, using cubical persistent homology, extracts high-level topological signatures that reflect the global structure of pixel intensities. We evaluate both methods on the large APTOS dataset for two classification tasks: binary detection (normal versus diabetic retinopathy) and five-class diabetic retinopathy severity grading. From each image, we extract 26244 HOG features and 800 TDA features, using them independently to train seven classical machine learning models with 10-fold cross-validation. XGBoost achieved the best performance in both cases: 94.29 percent accuracy (HOG) and 94.18 percent (TDA) on the binary task; 74.41 percent (HOG) and 74.69 percent (TDA) on the multi-class task. Our results show that both methods offer competitive performance but encode different structural aspects of the images. This is the first work to benchmark gradient-based and topological features on retinal imagery. The techniques are interpretable, applicable to other medical imaging domains, and suitable for integration into deep learning pipelines.

IntroductionThe urethra is a recommended avoidance structure for prostate cancer treatment. However, even subspecialist physicians often struggle to accurately identify the urethra on available imaging. Automated segmentation tools show promise, but a lack of reliable ground truth or appropriate evaluation standards has hindered validation and clinical adoption. This study aims to establish a reference-standard dataset with expert consensus contours, define clinically meaningful evaluation metrics, and assess the performance and generalizability of a deep-learning-based segmentation model. Materials and MethodsA multidisciplinary panel of four experienced subspecialists in prostate MRI generated consensus contours of the male urethra for 71 patients across six imaging centers. Four of those cases were previously used in an international study (PURE-MRI), wherein 62 physicians attempted to contour the prostate and urethra on the patient images. Separately, we developed a deep-learning AI model for urethra segmentation using another 151 cases from one center and evaluated it against the consensus reference standard and compared to human performance using Dice Score, percent urethra Coverage, and Maximum 2D (axial, in-plane) Hausdorff Distance (HD) from the reference standard. ResultsIn the PURE-MRI dataset, the AI model outperformed most physicians, achieving a median Dice of 0.41 (vs. 0.33 for physicians), Coverage of 81% (vs. 36%), and Max 2D HD of 1.8 mm (vs. 1.6 mm). In the larger dataset, performance remained consistent, with a Dice of 0.40, Coverage of 89%, and Max 2D HD of 2.0 mm, indicating strong generalizability across a broader patient population and more varied imaging conditions. ConclusionWe established a multidisciplinary consensus benchmark for segmentation of the urethra. The deep-learning model performs comparably to specialist physicians and demonstrates consistent results across multiple institutions. It shows promise as a clinical decision-support tool for accurate and reliable urethra segmentation in prostate cancer radiotherapy planning and studies of dose-toxicity associations.