<i>Objective.</i>This study investigates key factors influencing deep learning-based dose prediction models for head and neck cancer radiation therapy. The goal is to evaluate model accuracy, robustness, and computational efficiency, and to identify key components necessary for optimal performance.<i>Approach.</i>We systematically analyze the impact of input and dose grid resolution, input type, loss function, model architecture, and noise on model performance. Two datasets are used: a public dataset (OpenKBP) and an in-house clinical dataset. Model performance is primarily evaluated using two metrics: dose score and dose-volume histogram (DVH) score.<i>Main results.</i>High-resolution inputs improve prediction accuracy (dose score and DVH score) by 8.6%-13.5% compared to low resolution. Using a combination of CT, planning target volumes, and organs-at-risk as input significantly enhances accuracy, with improvements of 57.4%-86.8% over using CT alone. Integrating mean absolute error (MAE) loss with value-based and criteria-based DVH loss functions further boosts DVH score by 7.2%-7.5% compared to MAE loss alone. In the robustness analysis, most models show minimal degradation under Poisson noise (0-0.3 Gy) but are more susceptible to adversarial noise (0.2-7.8 Gy). Notably, certain models, such as SwinUNETR, demonstrate superior robustness against adversarial perturbations.<i>Significance.</i>These findings highlight the importance of optimizing deep learning models and provide valuable guidance for achieving more accurate and reliable radiotherapy dose prediction.

Medical anomaly detection (AD) is crucial for early clinical intervention, yet it faces challenges due to limited access to high-quality medical imaging data, caused by privacy concerns and data silos. Few-shot learning has emerged as a promising approach to alleviate these limitations by leveraging the large-scale prior knowledge embedded in vision-language models (VLMs). Recent advancements in few-shot medical AD have treated normal and abnormal cases as a one-class classification problem, often overlooking the distinction among multiple anomaly categories. Thus, in this paper, we propose a framework tailored for few-shot medical anomaly detection in the scenario where the identification of multiple anomaly categories is required. To capture the detailed radiological signs of medical anomaly categories, our framework incorporates diverse textual descriptions for each category generated by a Large-Language model, under the assumption that different anomalies in medical images may share common radiological signs in each category. Specifically, we introduce SD-MAD, a two-stage Sign-Driven few-shot Multi-Anomaly Detection framework: (i) Radiological signs are aligned with anomaly categories by amplifying inter-anomaly discrepancy; (ii) Aligned signs are selected further to mitigate the effect of the under-fitting and uncertain-sample issue caused by limited medical data, employing an automatic sign selection strategy at inference. Moreover, we propose three protocols to comprehensively quantify the performance of multi-anomaly detection. Extensive experiments illustrate the effectiveness of our method.

Accurate and efficient quantification of cardiac function is essential for the estimation of prognosis of cardiovascular diseases (CVDs). One of the most commonly used metrics for evaluating cardiac pumping performance is left ventricular ejection fraction (LVEF). However, LVEF can be affected by factors such as inter-observer variability and varying pre-load and after-load conditions, which can reduce its reproducibility. Additionally, cardiac dysfunction may not always manifest as alterations in LVEF, such as in heart failure and cardiotoxicity diseases. An alternative measure that can provide a relatively load-independent quantitative assessment of myocardial contractility is myocardial strain and strain rate. By using LVEF in combination with myocardial strain, it is possible to obtain a thorough description of cardiac function. Automated estimation of LVEF and other volumetric measures from cine-MRI sequences can be achieved through segmentation models, while strain calculation requires the estimation of tissue displacement between sequential frames, which can be accomplished using registration models. These tasks are often performed separately, potentially limiting the assessment of cardiac function. To address this issue, in this study we propose an end-to-end deep learning (DL) model that jointly estimates groupwise (GW) registration and segmentation for cardiac cine-MRI images. The proposed anatomically-guided Deep GW network was trained and validated on a large dataset of 4-chamber view cine-MRI image series of 374 subjects. A quantitative comparison with conventional GW registration using elastix and two DL-based methods showed that the proposed model improved performance and substantially reduced computation time.

To address the challenge of complex pathological feature extraction in automated cardiac MRI segmentation, this study proposes an innovative dual-encoder architecture named SAMba-UNet. The framework achieves cross-modal feature collaborative learning by integrating the vision foundation model SAM2, the state-space model Mamba, and the classical UNet. To mitigate domain discrepancies between medical and natural images, a Dynamic Feature Fusion Refiner is designed, which enhances small lesion feature extraction through multi-scale pooling and a dual-path calibration mechanism across channel and spatial dimensions. Furthermore, a Heterogeneous Omni-Attention Convergence Module (HOACM) is introduced, combining global contextual attention with branch-selective emphasis mechanisms to effectively fuse SAM2's local positional semantics and Mamba's long-range dependency modeling capabilities. Experiments on the ACDC cardiac MRI dataset demonstrate that the proposed model achieves a Dice coefficient of 0.9103 and an HD95 boundary error of 1.0859 mm, significantly outperforming existing methods, particularly in boundary localization for complex pathological structures such as right ventricular anomalies. This work provides an efficient and reliable solution for automated cardiac disease diagnosis, and the code will be open-sourced.

Computed Tomography (CT) scan, which produces 3D volumetric medical data that can be viewed as hundreds of cross-sectional images (a.k.a. slices), provides detailed anatomical information for diagnosis. For radiologists, creating CT radiology reports is time-consuming and error-prone. A visual question answering (VQA) system that can answer radiologists' questions about some anatomical regions on the CT scan and even automatically generate a radiology report is urgently needed. However, existing VQA systems cannot adequately handle the CT radiology question answering (CTQA) task for: (1) anatomic complexity makes CT images difficult to understand; (2) spatial relationship across hundreds slices is difficult to capture. To address these issues, this paper proposes CT-Agent, a multimodal agentic framework for CTQA. CT-Agent adopts anatomically independent tools to break down the anatomic complexity; furthermore, it efficiently captures the across-slice spatial relationship with a global-local token compression strategy. Experimental results on two 3D chest CT datasets, CT-RATE and RadGenome-ChestCT, verify the superior performance of CT-Agent.

As machine learning (ML) continuously develops in cancer diagnosis and treatment, some researchers have attempted to predict the expression of programmed death ligand-1 (PD-L1) in non-small cell lung cancer (NSCLC) by ML. However, there is a lack of systematic evidence on the effectiveness of ML. We conducted a thorough search across Embase, PubMed, the Cochrane Library, and Web of Science from inception to December 14th, 2023.A systematic review and meta-analysis was conducted to assess the value of ML for predicting PD-L1 expression in NSCLC. Totally 30 studies with 12,898 NSCLC patients were included. The thresholds of PD-L1 expression level were < 1%, 1-49%, and ≥ 50%. In the validation set, in the binary classification for PD-L1 ≥ 1%, the pooled C-index was 0.646 (95%CI: 0.587-0.705), 0.799 (95%CI: 0.782-0.817), 0.806 (95%CI: 0.753-0.858), and 0.800 (95%CI: 0.717-0.883), respectively, for the clinical feature-, radiomics-, radiomics + clinical feature-, and pathomics-based ML models; in the binary classification for PD-L1 ≥ 50%, the pooled C-index was 0.649 (95%CI: 0.553-0.744), 0.771 (95%CI: 0.728-0.814), and 0.826 (95%CI: 0.783-0.869), respectively, for the clinical feature-, radiomics-, and radiomics + clinical feature-based ML models. At present, radiomics- or pathomics-based ML methods are applied for the prediction of PD-L1 expression in NSCLC, which both achieve satisfactory accuracy. In particular, the radiomics-based ML method seems to have wider clinical applicability as a non-invasive diagnostic tool. Both radiomics and pathomics serve as processing methods for medical images. In the future, we expect to develop medical image-based DL methods for intelligently predicting PD-L1 expression.

To evaluate the variability of fully automated airway quantitative CT (QCT) measures caused by different kernels and the effect of kernel conversion. This retrospective study included 96 patients who underwent non-enhanced chest CT at two centers. CT scans were reconstructed using four kernels (medium soft, medium sharp, sharp, very sharp) from three vendors. Kernel conversion targeting the medium soft kernel as reference was applied to sharp kernel images. Fully automated airway quantification was performed before and after conversion. The effects of kernel type and conversion on airway quantification were evaluated using analysis of variance, paired t-tests, and concordance correlation coefficient (CCC). Airway QCT measures (e.g., Pi10, wall thickness, wall area percentage, lumen diameter) decreased with sharper kernels (all, p < 0.001), with varying degrees of variability across variables and vendors. Kernel conversion substantially reduced variability between medium soft and sharp kernel images for vendors A (pooled CCC: 0.59 vs. 0.92) and B (0.40 vs. 0.91) and lung-dedicated sharp kernels of vendor C (0.26 vs. 0.71). However, it was ineffective for non-lung-dedicated sharp kernels of vendor C (0.81 vs. 0.43) and showed limited improvement in variability of QCT measures at the subsegmental level. Consistent airway segmentation and identical anatomic labeling improved subsegmental airway variability in theoretical tests. Deep learning-based kernel conversion reduced the measurement variability of airway QCT across various kernels and vendors but was less effective for non-lung-dedicated kernels and subsegmental airways. Consistent airway segmentation and precise anatomic labeling can further enhance reproducibility for reliable automated quantification. Question How do different CT reconstruction kernels affect the measurement variability of automated airway measurements, and can deep learning-based kernel conversion reduce this variability? Findings Kernel conversion improved measurement consistency across vendors for lung-dedicated kernels, but showed limited effectiveness for non-lung-dedicated kernels and subsegmental airways. Clinical relevance Understanding kernel-related variability in airway quantification and mitigating it through deep learning enables standardized analysis, but further refinements are needed for robust airway segmentation, particularly for improving measurement variability in subsegmental airways and specific kernels.

Current brain tumor segmentation methods often utilize a U-Net architecture based on efficient convolutional neural networks. While effective, these architectures primarily model local dependencies, lacking the ability to capture global interactions like pure Transformer. However, using pure Transformer directly causes the network to lose local feature information. To address this limitation, we propose the Generative Adversarial Dilated Attention Convolutional Transformer(GDacFormer). GDacFormer enhances interactions between tumor regions while balancing global and local information through the integration of adversarial learning with an improved transformer module. Specifically, GDacFormer leverages a generative adversarial segmentation network to learn richer and more detailed features. It integrates a novel Transformer module, DacFormer, featuring multi-scale dilated attention and a next convolution block. This module, embedded within the generator, aggregates semantic multi-scale information, efficiently reduces the redundancy in the self-attention mechanism, and enhances local feature representations, thus refining the brain tumor segmentation results. GDacFormer achieves Dice values for whole tumor, core tumor, and enhancing tumor segmentation of 90.9%/90.8%/93.7%, 84.6%/85.7%/93.5%, and 77.9%/79.3%/86.3% on BraTS2019-2021 datasets. Extensive evaluations demonstrate the effectiveness and competitiveness of GDacFormer. The code for GDacFormer will be made publicly available at https://github.com/MuqinZ/GDacFormer.

This research aims to enhance X-ray lung segmentation by addressing boundary distortions in anatomical structures, with the objective of refining segmentation boundaries and improving the morphological shape of segmented objects. The proposed approach combines the K-segment principal curve with Gaussian Process Regression (GPR) to refine segmentation boundaries, evaluated using lung X-ray datasets at varying resolutions. Several state-of-the-art models, including U-Net, SegNet, and TransUnet, were also assessed for comparison. The model employed a custom kernel for GPR, combining Radial Basis Function (RBF) with a cosine similarity term. The effectiveness of the model was evaluated using metrics such as the Dice Coefficient (DC) and Jaccard Index (JC) for segmentation accuracy, along with Average Symmetric Surface Distance (ASSD) and Hausdorff Distance (HD) for boundary alignment. The proposed method achieved superior segmentation performance, particularly at the highest resolution (1024x1024 pixels), with a DC of 95.7% for the left lung and 94.1% for the right lung. Among the different models, TransUnet outperformed others across both the semantic segmentation and boundary refinement stages, showing significant improvements in DC, JC, ASSD, and HD. The results indicate that the proposed boundary refinement approach effectively improves the segmentation quality of lung X-rays, excelling in refining well-defined structures and achieving superior boundary alignment, showcasing its potential for clinical applications. However, limitations exist when dealing with irregular or unpredictable shapes, suggesting areas for future enhancement.

Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder featuring impaired social interactions and communication abilities engaging the individuals in a restrictive or repetitive behaviour. Though incurable early detection and intervention can reduce the severity of symptoms. Structural magnetic resonance imaging (sMRI) can improve diagnostic accuracy, facilitating early diagnosis to offer more tailored care. With the emergence of deep learning (DL), neuroimaging-based approaches for ASD diagnosis have been focused. However, many existing models lack interpretability of their decisions for diagnosis. The prime objective of this work is to perform ASD classification precisely and to interpret the classification process in a better way so as to discern the major features that are appropriate for the prediction of disorder. The proposed model employs neural architecture search network - mobile(NASNet-Mobile) model for ASD detection, which is integrated with an explainable artificial intelligence (XAI) technique called local interpretable model-agnostic explanations (LIME) for increased transparency of ASD classification. The model is trained on sMRI images of two age groups taken from autism brain imaging data exchange-I (ABIDE-I) dataset. The proposed model yielded accuracy of 0.9607, F1-score of 0.9614, specificity of 0.9774, sensitivity of 0.9451, negative predicted value (NPV) of 0.9429, positive predicted value (PPV) of 0.9783 and the diagnostic odds ratio of 745.59 for 2 to 11 years age group compared to 12 to 18 years group. These results are superior compared to other state of the art models Inception v3 and SqueezeNet.