Artificial intelligence (AI) can enhance the diagnostic performance of breast cancer imaging and improve workflow optimization, potentially mitigating excessive radiologist workload and suboptimal diagnostic accuracy. AI can also boost imaging capabilities through individual risk prediction, molecular subtyping, and neoadjuvant therapy response predictions. Evidence demonstrates AI's potential across multiple modalities. The most robust data come from mammographic screening, where AI models improve diagnostic accuracy and optimize workflow, but rigorous post-market surveillance is required before any implementation strategy in this field. Commercial tools for digital breast tomosynthesis and ultrasound, potentially able to reduce interpretation time and improve accuracy, are also available, but post-implementation evaluation studies are likewise lacking. Besides basic tools for breast MRI with limited proven clinical benefit, AI applications for other modalities are not yet commercially available. Applications in contrast-enhanced mammography are still in the research stage, especially for radiomics-based molecular subtype classification. Applications of Large Language Models (LLMs) are in their infancy, and there are currently no clinical applications. Consequently, and despite their promise, all commercially available AI tools for breast imaging should currently still be regarded as techniques that, at best, aid radiologists in image evaluation. Their use is therefore optional, and the findings may always be overruled. KEY POINTS: AI systems improve diagnostic accuracy and efficiency of mammography screening, but long-term outcomes data are lacking. Commercial tools for digital breast tomosynthesis and ultrasound are available, but post-implementation evaluation studies are lacking. AI tools for breast imaging should still be regarded as a non-obligatory aid to radiologists for image interpretation.

To develop a nomogram integrating clinical and multimodal MRI features for non-invasive prediction of microsatellite instability (MSI) in endometrial cancer (EC), and to evaluate its diagnostic performance. This retrospective multicenter study included 216 EC patients (mean age, 54.68 ± 8.72 years) from two institutions (2017-2023). Patients were classified as MSI (n=59) or microsatellite stable (MSS, n=157) based on immunohistochemistry. Institution A data were randomly split into training (n=132) and testing (n=33) sets (8:2 ratio), while Institution B data (n=51) served as external validation. Eight machine learning algorithms were used to construct models. A nomogram combining radiomics score and clinical predictors was developed. Performance was evaluated via receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA). The T2-weighted imaging (T2WI) radiomics model showed the highest area under the receiver operating characteristic curve (AUC) among single sequences (training set:0.908; test set:0.838). The combined-sequence radiomics model achieved superior performance (AUC: training set=0.983, test set=0.862). The support vector machine (SVM) outperformed other algorithms. The nomogram integrating rad-score and clinical features demonstrated higher predictive efficacy than the clinical model (test set: AUC=0.904 vs. 0.654; p < 0.05) and comparable to the multimodal radiomics model. DCA indicated significant clinical utility for both nomogram and radiomics models. The clinical-radiomics nomogram effectively predicts MSI status in EC, offering a non-invasive tool for guiding immunotherapy decisions.

Accurate sarcopenia diagnosis via ultrasound remains challenging due to subtle imaging cues, limited labeled data, and the absence of clinical context in most models. We propose MedVQA-TREE, a multimodal framework that integrates a hierarchical image interpretation module, a gated feature-level fusion mechanism, and a novel multi-hop, multi-query retrieval strategy. The vision module includes anatomical classification, region segmentation, and graph-based spatial reasoning to capture coarse, mid-level, and fine-grained structures. A gated fusion mechanism selectively integrates visual features with textual queries, while clinical knowledge is retrieved through a UMLS-guided pipeline accessing PubMed and a sarcopenia-specific external knowledge base. MedVQA-TREE was trained and evaluated on two public MedVQA datasets (VQA-RAD and PathVQA) and a custom sarcopenia ultrasound dataset. The model achieved up to 99% diagnostic accuracy and outperformed previous state-of-the-art methods by over 10%. These results underscore the benefit of combining structured visual understanding with guided knowledge retrieval for effective AI-assisted diagnosis in sarcopenia.

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders worldwide. As it progresses, it leads to the deterioration of cognitive functions. Since AD is irreversible, early diagnosis is crucial for managing its progression. Mild Cognitive Impairment (MCI) represents an intermediate stage between Cognitively Normal (CN) individuals and those with AD, and is considered a transitional phase from normal cognition to Alzheimer's disease. Diagnosing MCI is particularly challenging due to the subtle differences between adjacent diagnostic categories. In this study, we propose EffNetViTLoRA, a generalized end-to-end model for AD diagnosis using the whole Alzheimer's Disease Neuroimaging Initiative (ADNI) Magnetic Resonance Imaging (MRI) dataset. Our model integrates a Convolutional Neural Network (CNN) with a Vision Transformer (ViT) to capture both local and global features from MRI images. Unlike previous studies that rely on limited subsets of data, our approach is trained on the full T1-weighted MRI dataset from ADNI, resulting in a more robust and unbiased model. This comprehensive methodology enhances the model's clinical reliability. Furthermore, fine-tuning large pretrained models often yields suboptimal results when source and target dataset domains differ. To address this, we incorporate Low-Rank Adaptation (LoRA) to effectively adapt the pretrained ViT model to our target domain. This method enables efficient knowledge transfer and reduces the risk of overfitting. Our model achieves a classification accuracy of 92.52% and an F1-score of 92.76% across three diagnostic categories: AD, MCI, and CN for full ADNI dataset.

Thyroid hormones are significant for controlling metabolism, and two common thyroid disorders, such as hypothyroidism. The hyperthyroidism are directly affect the metabolic rate of the human body. Predicting and diagnosing thyroid disease remain significant challenges in medical research due to the complexity of thyroid hormone regulation and its impact on metabolism. Therefore, this paper proposes a Complex-valued Spatio-Temporal Graph Convolution Neural Network optimized with Giraffe Kicking Optimization Algorithm for Thyroid Nodule Classification in Ultrasound Images (CSGCNN-GKOA-TNC-UI). Here, the ultrasound images are collected through DDTI (Digital Database of Thyroid ultrasound Imageries) dataset. The gathered data is given into the pre-processing stage using Bilinear Double-Order Filter (BDOF) approach to eradicate the noise and increase the input images quality. The pre-processing image is given into the Deep Adaptive Fuzzy Clustering (DAFC) for Region of Interest (RoI) segmentation. The segmented image is fed to the Multi-Objective Matched Synchro Squeezing Chirplet Transform (MMSSCT) for extracting the features, like Geometric features and Morphological features. The extracted features are fed into the CSGCNN, which classifies the Thyroid Nodule into Benign Nodules and Malign Nodules. Finally, Giraffe Kicking Optimization Algorithm (GKOA) is considered to enhance the CSGCNN classifier. The CSGCNN-GKOA-TNC-UI algorithm is implemented in MATLAB. The CSGCNN-GKOA-TNC-UI approach attains 34.9%, 21.5% and 26.8% higher f-score, 18.6%, 29.3 and 19.2% higher accuracy when compared with existing models: Thyroid diagnosis with classification utilizing DNN depending on hybrid meta-heuristic with LSTM method (LSTM-TNC-UI), innovative full-scale connected network for segmenting thyroid nodule in UI (FCG Net-TNC-UI), and Adversarial architecture dependent multi-scale fusion method for segmenting thyroid nodule (AMSeg-TNC-UI) methods respectively. The proposed model enhances thyroid nodule classification accuracy, aiding radiologists and endocrinologists. By reducing misclassification, it minimizes unnecessary biopsies and enables early malignancy detection.

Enhancing the resolution of Magnetic Resonance Imaging (MRI) through super-resolution (SR) reconstruction is crucial for boosting diagnostic precision. However, current SR methods primarily rely on single LR images or multi-contrast features, limiting detail restoration. Inspired by video frame interpolation, this work utilizes the spatiotemporal correlations between adjacent slices to reformulate the SR task of anisotropic 3D-MRI image into the generation of new high-resolution (HR) slices between adjacent 2D slices. The generated SR slices are subsequently combined with the HR adjacent slices to create a new HR 3D-MRI image. We propose a innovative network architecture termed DGWMSR, comprising a backbone network and a feature supplement module (FSM). The backbone's core innovations include the displacement former block (DFB) module, which independently extracts structural and displacement features, and the maskdisplacement vector network (MDVNet) which combines with Warp mechanism to facilitate edge pixel detailing. The DFB integrates the inter-slice attention (ISA) mechanism into the Transformer, effectively minimizing the mutual interference between the two types of features and mitigating volume effects during reconstruction. Additionally, the FSM module combines self-attention with feed-forward neural network, which emphasizes critical details derived from the backbone architecture. Experimental results demonstrate the DGWMSR network outperforms current MRI SR methods on Kirby21, ANVIL-adult, and MSSEG datasets. Our code has been made publicly available on GitHub at https://github.com/Dohbby/DGWMSR.

Reliable medical image synthesis is crucial for clinical applications and downstream tasks, where high-quality anatomical structure and predictive confidence are essential. Existing studies have made significant progress by embedding prior conditional knowledge, such as conditional images or textual information, to synthesize natural images. However, medical image synthesis remains a challenging task due to: 1) Data scarcity: High-quality medical text prompt are extremely rare and require specialized expertise. 2) Insufficient uncertainty estimation: The uncertainty estimation is critical for evaluating the confidence of reliable medical image synthesis. This paper presents a novel approach for medical image synthesis, driven by radiomics prompts and combined with Monte Carlo Compression Sampling (MCCS) to ensure reliability. For the first time, our method leverages clinically focused radiomics prompts to condition the generation process, guiding the model to produce reliable medical images. Furthermore, the innovative MCCS algorithm employs Monte Carlo methods to randomly select and compress sampling steps within the denoising diffusion implicit models (DDIM), enabling efficient uncertainty quantification. Additionally, we introduce a MambaTrans architecture to model long-range dependencies in medical images and embed prior conditions (e.g., radiomics prompts). Extensive experiments on benchmark medical imaging datasets demonstrate that our approach significantly improves image quality and reliability, outperforming SoTA methods in both qualitative and quantitative evaluations.

Large annotated datasets are vital for training segmentation models, but pixel-level labeling is time-consuming, error-prone, and often requires scarce expert annotators, especially in medical imaging. In contrast, coarse annotations are quicker, cheaper, and easier to produce, even by non-experts. In this paper, we propose to use coarse drawings from both positive (target) and negative (background) classes in the image, even with noisy pixels, to train a convolutional neural network (CNN) for semantic segmentation. We present a method for learning the true segmentation label distributions from purely noisy coarse annotations using two coupled CNNs. The separation of the two CNNs is achieved by high fidelity with the characters of the noisy training annotations. We propose to add a complementary label learning that encourages estimating negative label distribution. To illustrate the properties of our method, we first use a toy segmentation dataset based on MNIST. We then present the quantitative results of experiments using publicly available datasets: Cityscapes dataset for multi-class segmentation, and retinal images for medical applications. In all experiments, our method outperforms state-of-the-art methods, particularly in the cases where the ratio of coarse annotations is small compared to the given dense annotations.

With the exponential growth of big data in domains such as telemedicine and digital forensics, the secure transmission of sensitive medical information has become a critical concern. Conventional steganographic methods often fail to maintain diagnostic integrity or exhibit robustness against noise and transformations. In this study, we propose a novel deep learning-based steganographic framework that combines Squeeze-and-Excitation (SE) blocks, Inception modules, and residual connections to address these challenges. The encoder integrates dilated convolutions and SE attention to embed secret medical images within natural cover images, while the decoder employs residual and multi-scale Inception-based feature extraction for accurate reconstruction. Designed for deployment on NVIDIA Jetson TX2, the model ensures real-time, low-power operation suitable for edge healthcare applications. Experimental evaluation on MRI and OCT datasets demonstrates the model's efficacy, achieving Peak Signal-to-Noise Ratio (PSNR) values of 39.02 and 38.75, and Structural Similarity Index (SSIM) values of 0.9757, confirming minimal visual distortion. This research contributes to advancing secure, high-capacity steganographic systems for practical use in privacy-sensitive environments.

Functional hyperaemia is a well-established hallmark of healthy brain function, whereby local brain blood flow adjusts in response to a change in the activity of the surrounding neurons. Although functional hyperemia has been extensively studied at the level of both tissue and individual vessels, vascular network-level coordination remains largely unknown. To bridge this gap, we developed a deep learning-based computational pipeline that uses two-photon fluorescence microscopy images of cerebral microcirculation to enable automated reconstruction and quantification of the geometric changes across the microvascular network, comprising hundreds of interconnected blood vessels, pre and post-activation of the neighbouring neurons. The pipeline's utility was demonstrated in the Thy1-ChR2 optogenetic mouse model, where we observed network-wide vessel radius changes to depend on the photostimulation intensity, with both dilations and constrictions occurring across the cortical depth, at an average of 16.1{+/-}14.3 m (mean{+/-}stddev) away from the most proximal neuron for dilations; and at 21.9{+/-}14.6 m away for constrictions. We observed a significant heterogeneity of the vascular radius changes within vessels, with radius adjustment varying by an average of 24 {+/-} 28% of the resting diameter, likely reflecting the heterogeneity of the distribution of contractile cells on the vessel walls. A graph theory-based network analysis revealed that the assortativity of adjacent blood vessel responses rose by 152 {+/-} 65% at 4.3 mW/mm2 of blue photostimulation vs. the control, with a 4% median increase in the efficiency of the capillary networks during this level of blue photostimulation in relation to the baseline. Interrogating individual vessels is thus not sufficient to predict how the blood flow is modulated in the network. Our computational pipeline, to be made openly available, enables tracking of the microvascular network geometry over time, relating caliber adjustments to vessel wall-associated cells' state, and mapping network-level flow distribution impairments in experimental models of disease.