Accurate and reliable brain tumor segmentation, particularly when dealing with missing modalities, remains a critical challenge in medical image analysis. Previous studies have not fully resolved the challenges of tumor boundary segmentation insensitivity and feature transfer in the absence of key imaging modalities. In this study, we introduce MST-KDNet, aimed at addressing these critical issues. Our model features Multi-Scale Transformer Knowledge Distillation to effectively capture attention weights at various resolutions, Dual-Mode Logit Distillation to improve the transfer of knowledge, and a Global Style Matching Module that integrates feature matching with adversarial learning. Comprehensive experiments conducted on the BraTS and FeTS 2024 datasets demonstrate that MST-KDNet surpasses current leading methods in both Dice and HD95 scores, particularly in conditions with substantial modality loss. Our approach shows exceptional robustness and generalization potential, making it a promising candidate for real-world clinical applications. Our source code is available at https://github.com/Quanato607/MST-KDNet.

Performance monitoring is essential for safe clinical deployment of image classification models. However, because ground-truth labels are typically unavailable in the target dataset, direct assessment of real-world model performance is infeasible. State-of-the-art performance estimation methods address this by leveraging confidence scores to estimate the target accuracy. Despite being a promising direction, the established methods mainly estimate the model's accuracy and are rarely evaluated in a clinical domain, where strong class imbalances and dataset shifts are common. Our contributions are twofold: First, we introduce generalisations of existing performance prediction methods that directly estimate the full confusion matrix. Then, we benchmark their performance on chest x-ray data in real-world distribution shifts as well as simulated covariate and prevalence shifts. The proposed confusion matrix estimation methods reliably predicted clinically relevant counting metrics on medical images under distribution shifts. However, our simulated shift scenarios exposed important failure modes of current performance estimation techniques, calling for a better understanding of real-world deployment contexts when implementing these performance monitoring techniques for postmarket surveillance of medical AI models.

This study proposes a deep learning-based framework for automated segmentation of brain regions and classification of amyloid positivity using positron emission tomography (PET) images alone, without the need for structural MRI or CT. A 3D U-Net architecture with four layers of depth was trained and validated on a dataset of 200 F18-florbetapir amyloid-PET scans, with an 130/20/50 train/validation/test split. Segmentation performance was evaluated using Dice similarity coefficients across 30 brain regions, with scores ranging from 0.45 to 0.88, demonstrating high anatomical accuracy, particularly in subcortical structures. Quantitative fidelity of PET uptake within clinically relevant regions. Precuneus, prefrontal cortex, gyrus rectus, and lateral temporal cortex was assessed using normalized root mean square error, achieving values as low as 0.0011. Furthermore, the model achieved a classification accuracy of 0.98 for amyloid positivity based on regional uptake quantification, with an area under the ROC curve (AUC) of 0.99. These results highlight the model's potential for integration into PET only diagnostic pipelines, particularly in settings where structural imaging is not available. This approach reduces dependence on coregistration and manual delineation, enabling scalable, reliable, and reproducible analysis in clinical and research applications. Future work will focus on clinical validation and extension to diverse PET tracers including C11 PiB and other F18 labeled compounds.

We propose a learnable variational model that learns the features and leverages complementary information from both image and measurement domains for image reconstruction. In particular, we introduce a learned alternating minimization algorithm (LAMA) from our prior work, which tackles two-block nonconvex and nonsmooth optimization problems by incorporating a residual learning architecture in a proximal alternating framework. In this work, our goal is to provide a complete and rigorous convergence proof of LAMA and show that all accumulation points of a specified subsequence of LAMA must be Clarke stationary points of the problem. LAMA directly yields a highly interpretable neural network architecture called LAMA-Net. Notably, in addition to the results shown in our prior work, we demonstrate that the convergence property of LAMA yields outstanding stability and robustness of LAMA-Net in this work. We also show that the performance of LAMA-Net can be further improved by integrating a properly designed network that generates suitable initials, which we call iLAMA-Net. To evaluate LAMA-Net/iLAMA-Net, we conduct several experiments and compare them with several state-of-the-art methods on popular benchmark datasets for Sparse-View Computed Tomography.

A fundamental challenge in neuroscience is to decode mental states from brain activity. While functional magnetic resonance imaging (fMRI) offers a non-invasive approach to capture brain-wide neural dynamics with high spatial precision, decoding from fMRI data -- particularly from task-evoked activity -- remains challenging due to its high dimensionality, low signal-to-noise ratio, and limited within-subject data. Here, we leverage recent advances in computer vision and propose STDA-SwiFT, a transformer-based model that learns transferable representations from large-scale fMRI datasets via spatial-temporal divided attention and self-supervised contrastive learning. Using pretrained voxel-wise representations from 995 subjects in the Human Connectome Project (HCP), we show that our model substantially improves downstream decoding performance of task-evoked activity across multiple sensory and cognitive domains, even with minimal data preprocessing. We demonstrate performance gains from larger receptor fields afforded by our memory-efficient attention mechanism, as well as the impact of functional relevance in pretraining data when fine-tuning on small samples. Our work showcases transfer learning as a viable approach to harness large-scale datasets to overcome challenges in decoding brain activity from fMRI data.

Accurate identification of late-life depression (LLD) using structural brain MRI is essential for monitoring disease progression and facilitating timely intervention. However, existing learning-based approaches for LLD detection are often constrained by limited sample sizes (e.g., tens), which poses significant challenges for reliable model training and generalization. Although incorporating auxiliary datasets can expand the training set, substantial domain heterogeneity, such as differences in imaging protocols, scanner hardware, and population demographics, often undermines cross-domain transferability. To address this issue, we propose a Collaborative Domain Adaptation (CDA) framework for LLD detection using T1-weighted MRIs. The CDA leverages a Vision Transformer (ViT) to capture global anatomical context and a Convolutional Neural Network (CNN) to extract local structural features, with each branch comprising an encoder and a classifier. The CDA framework consists of three stages: (a) supervised training on labeled source data, (b) self-supervised target feature adaptation and (c) collaborative training on unlabeled target data. We first train ViT and CNN on source data, followed by self-supervised target feature adaptation by minimizing the discrepancy between classifier outputs from two branches to make the categorical boundary clearer. The collaborative training stage employs pseudo-labeled and augmented target-domain MRIs, enforcing prediction consistency under strong and weak augmentation to enhance domain robustness and generalization. Extensive experiments conducted on multi-site T1-weighted MRI data demonstrate that the CDA consistently outperforms state-of-the-art unsupervised domain adaptation methods.

Accurate differential diagnosis of pneumonia remains a challenging task, as different types of pneumonia require distinct treatment strategies. Early and precise diagnosis is crucial for minimizing the risk of misdiagnosis and for effectively guiding clinical decision-making and monitoring treatment response. This study proposes the WSDC-ViT network to enhance computer-aided pneumonia detection and alleviate the diagnostic workload for radiologists. Unlike existing models such as Swin Transformer or CoAtNet, which primarily improve attention mechanisms through hierarchical designs or convolutional embedding, WSDC-ViT introduces a novel architecture that simultaneously enhances global and local feature extraction through a scalable self-attention mechanism and convolutional refinement. Specifically, the network integrates a scalable self-attention mechanism that decouples the query, key, and value dimensions to reduce computational overhead and improve contextual learning, while an interactive window-based attention module further strengthens long-range dependency modeling. Additionally, a convolution-based module equipped with a dynamic ReLU activation function is embedded within the transformer encoder to capture fine-grained local details and adaptively enhance feature expression. Experimental results demonstrate that the proposed method achieves an average classification accuracy of 95.13% and an F1-score of 95.63% on a chest X-ray dataset, along with 99.36% accuracy and a 99.34% F1-score on a CT dataset. These results highlight the model's superior performance compared to existing automated pneumonia classification approaches, underscoring its potential clinical applicability.

This study aimed to develop and validate a multi-temporal magnetic resonance imaging (MRI)-based delta-radiomics model to accurately predict severe acute radiation enteritis risk in patients undergoing total neoadjuvant therapy (TNT) for locally advanced rectal cancer (LARC). A retrospective analysis was conducted on the data from 92 patients with LARC who received TNT. All patients underwent pelvic MRI at baseline (pre-treatment) and after neoadjuvant radiotherapy (post-RT). Radiomic features of the primary tumor region were extracted from T2-weighted images at both timepoints. Four delta feature strategies were defined (absolute difference, percent change, ratio, and feature fusion) by concatenating pre- and post-RT features. Severe acute radiation enteritis (SARE) was defined as a composite CTCAE-based symptom score of ≥ 3 within the first 2 weeks of radiotherapy. Features were selected via statistical evaluation and least absolute shrinkage and selection operator regression. Support vector machine (SVM) classifiers were trained using baseline, post-RT, delta, and combined radiomic and clinical features. Model performance was evaluated in an independent test set based on the area under the curve (AUC) value and other metrics. Only the delta-fusion strategy retained stable radiomic features after selection, and outperformed the difference, percent, and ratio definitions in terms of feature stability and model performance. The SVM model, based on combined delta-fusion radiomics and clinical variables, demonstrated the best predictive performance and generalizability. In the independent test cohort, this combined model demonstrated an AUC value of 0.711, sensitivity of 88.9%, and F1-score of 0.696; these values surpassed those of models built with baseline-only or delta difference features. Integrating multi-temporal radiomic features via delta-fusion with clinical factors markedly improved early prediction of SARE in LARC. The delta-fusion approach outperformed conventional delta calculations, and demonstrated superior predictive performance. This highlights its potential in guiding individualized TNT sequencing and proactive toxicity management. NA.

Radiation-induced xerostomia is a common sequela in patients who undergo head and neck radiation therapy. This study aims to develop a three-dimensional deep learning model to predict xerostomia by fusing data from the gross tumor volume primary (GTVp) channel and parotid glands (PGs) channel. Retrospective data were collected from 180 head and neck cancer patients. Xerostomia was defined as xerostomia of grade ≥ 2 occurring in the 6th month of radiation therapy. The dataset was split into 137 cases (58.4% xerostomia, 41.6% non-xerostomia) for training and 43 (55.8% xerostomia, 44.2% non-xerostomia) for testing. XeroNet was composed of GNet, PNet, and a Naive Bayes decision fusion layer. GNet processed data from the GTVp channel (CT, dose distributions corresponding and the GTVp contours). PNet processed data from the PGs channel (CT, dose distributions and the PGs contours). The Naive Bayes decision fusion layer was used to integrate the results from GNet and PNet. Model performance was evaluated using accuracy, F-score, sensitivity, specificity, and area under the receiver operator characteristic curve (AUC). The proposed model achieved promising prediction results. The accuracy, AUC, F-score, sensitivity and specificity were 0.779, 0.858, 0.797, 0.777, and 0.782, respectively. Features extracted from the CT and dose distributions in the GTVp and PGs regions were used to construct machine learning models. However, the performance of these models was inferior to our method. Compared with recent studies on xerostomia prediction, our method also showed better performance. The proposed model could effectively extract features from the GTVp and PGs channels, achieving good performance in xerostomia prediction.

Neoadjuvant therapy plays a pivotal role in breast cancer treatment, particularly for patients aiming to conserve their breast by reducing tumor size pre-surgery. The ultimate goal of this treatment is achieving a pathologic complete response (pCR), which signifies the complete eradication of cancer cells, thereby lowering the likelihood of recurrence. This study introduces a novel predictive approach to identify patients likely to achieve pCR using radiomic features extracted from MR images, enhanced by the InceptionV3 model and cutting-edge validation methodologies. In our study, we gathered data from 255 unique Patient IDs sourced from the -SPY 2 MRI database with the goal of classifying pCR (pathological complete response). Our research introduced two key areas of novelty.Firstly, we explored the extraction of advanced features from the dcom series such as Area, Perimeter, Entropy, Intensity of the places where the intensity is more than the average intensity of the image. These features provided deeper insights into the characteristics of the MRI data and enhanced the discriminative power of our classification model.Secondly, we applied these extracted features along with combine pixel array of the dcom series of each patient to the numerous deep learning model along with InceptionV3 (GoogleNet) model which provides the best accuracy. To optimize the model's performance, we experimented with different combinations of loss functions, optimizer functions, and activation functions. Lastly, our classification results were subjected to validation using accuracy, AUC, Sensitivity, Specificity and F1 Score. These evaluation metrics provided a robust assessment of the model's performance and ensured the reliability of our findings. The successful combination of advanced feature extraction, utilization of the InceptionV3 model with tailored hyperparameters, and thorough validation using cutting-edge techniques significantly enhanced the accuracy and reliability of our pCR classification study. By adopting a collaborative approach that involved both radiologists and the computer-aided system, we achieved superior predictive performance for pCR, as evidenced by the impressive values obtained for the area under the curve (AUC) at 0.91 having an accuracy of .92. Overall, the combination of advanced feature extraction, leveraging the InceptionV3 model with customized hyperparameters, and rigorous validation using state-of-the-art techniques contributed to the accuracy and credibility of our pCR classification study.