This study aimed to compare three publicly available deep learning models (TotalSegmentator, TotalVibeSegmentator, and PanSegNet) for automated pancreatic segmentation on magnetic resonance images and to evaluate their performance against human annotations in terms of segmentation accuracy, volumetric measurement, and intrapancreatic fat fraction (IPFF) assessment. Twenty upper abdominal T1-weighted magnetic resonance series acquired using the two-point Dixon method were randomly selected. Three radiologists manually segmented the pancreas, and a ground-truth mask was constructed through a majority vote per voxel. Pancreatic segmentation was also performed using the three artificial intelligence models. Performance was evaluated using the Dice similarity coefficient (DSC), 95th-percentile Hausdorff distance, average symmetric surface distance, positive predictive value, sensitivity, Bland-Altman plots, and concordance correlation coefficient (CCC) for pancreatic volume and IPFF. PanSegNet achieved the highest DSC (mean ± standard deviation, 0.883 ± 0.095) and showed no statistically significant difference from the human interobserver DSC (0.896 ± 0.068; p = 0.24). In contrast, TotalVibeSegmentator (0.731 ± 0.105) and TotalSegmentator (0.707 ± 0.142) had significantly lower DSC values compared with the human interobserver average (p < 0.001). For pancreatic volume and IPFF, PanSegNet demonstrated the best agreement with the ground truth (CCC values of 0.958 and 0.993, respectively), followed by TotalSegmentator (0.834 and 0.980) and TotalVibeSegmentator (0.720 and 0.672). PanSegNet demonstrated the highest segmentation accuracy and the best agreement with human measurements for both pancreatic volume and IPFF on T1-weighted Dixon images. This model appears to be the most suitable for large-scale studies requiring automated pancreatic segmentation and intrapancreatic fat evaluation.

Prostate cancer (PCa) is one of the most common types of cancer, seriously affecting adult male health. Accurate and automated PCa segmentation is essential for radiologists to confirm the location of cancer, evaluate its severity, and design appropriate treatments. This paper presents PCaSAM, a fully automated PCa segmentation model that allows us to input multi-modal MRI images into the foundation model to improve performance significantly. We collected multi-center datasets to conduct a comprehensive evaluation. The results showed that PCaSAM outperforms the generalist medical foundation model and the other representative segmentation models, with the average DSC of 0.721 and 0.706 in the internal and external datasets, respectively. Furthermore, with the assistance of segmentation, the PI-RADS scoring of PCa lesions was improved significantly, leading to a substantial increase in average AUC by 8.3-8.9% on two external datasets. Besides, PCaSAM achieved superior efficiency, making it highly suitable for real-world deployment scenarios.

This study compares the classification accuracy of novel transformer-based deep learning models (ViT and BEiT) on brain MRIs of gliomas and meningiomas through a feature-driven approach. Meta's Segment Anything Model was used for semi-automatic segmentation, therefore proposing a total neural network-based workflow for this classification task. ViT and BEiT models were finetuned to a publicly available brain MRI dataset. Gliomas/meningiomas cases (625/507) were used for training and 520 cases (260/260; gliomas/meningiomas) for testing. The extracted deep radiomic features from ViT and BEiT underwent normalization, dimensionality reduction based on the Pearson correlation coefficient (PCC), and feature selection using analysis of variance (ANOVA). A multi-layer perceptron (MLP) with 1 hidden layer, 100 units, rectified linear unit activation, and Adam optimizer was utilized. Hyperparameter tuning was performed via 5-fold cross-validation. The ViT model achieved the highest AUC on the validation dataset using 7 features, yielding an AUC of 0.985 and accuracy of 0.952. On the independent testing dataset, the model exhibited an AUC of 0.962 and an accuracy of 0.904. The BEiT model yielded an AUC of 0.939 and an accuracy of 0.871 on the testing dataset. This study demonstrates the effectiveness of transformer-based models, especially ViT, for glioma and meningioma classification, achieving high AUC scores and accuracy. However, the study is limited by the use of a single dataset, which may affect generalizability. Future work should focus on expanding datasets and further optimizing models to improve performance and applicability across different institutions. This study introduces a feature-driven methodology for glioma and meningioma classification, showcasing advancements in the accuracy and model robustness of transformer-based models.

Recent advancements in diffusion probabilistic models (DPMs) have revolutionized image processing, demonstrating significant potential in medical applications. Accurate segmentation of the left ventricle (LV) in echocardiograms is crucial for diagnostic procedures and necessary treatments. However, ultrasound images are notoriously noisy with low contrast and ambiguous LV boundaries, thereby complicating the segmentation process. To address these challenges, this paper introduces Echo-DND, a novel dual-noise diffusion model specifically designed for this task. Echo-DND leverages a unique combination of Gaussian and Bernoulli noises. It also incorporates a multi-scale fusion conditioning module to improve segmentation precision. Furthermore, it utilizes spatial coherence calibration to maintain spatial integrity in segmentation masks. The model's performance was rigorously validated on the CAMUS and EchoNet-Dynamic datasets. Extensive evaluations demonstrate that the proposed framework outperforms existing SOTA models. It achieves high Dice scores of 0.962 and 0.939 on these datasets, respectively. The proposed Echo-DND model establishes a new standard in echocardiogram segmentation, and its architecture holds promise for broader applicability in other medical imaging tasks, potentially improving diagnostic accuracy across various medical domains. Project page: https://abdur75648.github.io/Echo-DND

Ensuring reliability is paramount in deep learning, particularly within the domain of medical imaging, where diagnostic decisions often hinge on model outputs. The capacity to separate out-of-distribution (OOD) samples has proven to be a valuable indicator of a model's reliability in research. In medical imaging, this is especially critical, as identifying OOD inputs can help flag potential anomalies that might otherwise go undetected. While many OOD detection methods rely on feature or logit space representations, recent works suggest these approaches may not fully capture OOD diversity. To address this, we propose a novel OOD scoring mechanism, called NERO, that leverages neuron-level relevance at the feature layer. Specifically, we cluster neuron-level relevance for each in-distribution (ID) class to form representative centroids and introduce a relevance distance metric to quantify a new sample's deviation from these centroids, enhancing OOD separability. Additionally, we refine performance by incorporating scaled relevance in the bias term and combining feature norms. Our framework also enables explainable OOD detection. We validate its effectiveness across multiple deep learning architectures on the gastrointestinal imaging benchmarks Kvasir and GastroVision, achieving improvements over state-of-the-art OOD detection methods.

Medical imaging plays a crucial role in assessing knee osteoarthritis (OA) risk by enabling early detection and disease monitoring. Recent machine learning methods have improved risk estimation (i.e., predicting the likelihood of disease progression) and predictive modelling (i.e., the forecasting of future outcomes based on current data) using medical images, but clinical adoption remains limited due to their lack of interpretability. Existing approaches that generate future images for risk estimation are complex and impractical. Additionally, previous methods fail to localize anatomical knee landmarks, limiting interpretability. We address these gaps with a new interpretable machine learning method to estimate the risk of knee OA progression via multi-task predictive modelling that classifies future knee OA severity and predicts anatomical knee landmarks from efficiently generated high-quality future images. Such image generation is achieved by leveraging a diffusion model in a class-conditioned latent space to forecast disease progression, offering a visual representation of how particular health conditions may evolve. Applied to the Osteoarthritis Initiative dataset, our approach improves the state-of-the-art (SOTA) by 2\%, achieving an AUC of 0.71 in predicting knee OA progression while offering ~9% faster inference time.

This comprehensive review examines the current state and evolution of artificial intelligence applications in colorectal cancer detection through medical imaging from 2019 to 2025. The study presents a quantitative analysis of 110 high-quality publications and 9 publicly accessible medical image datasets used for training and validation. Various convolutional neural network architectures-including ResNet (40 implementations), VGG (18 implementations), and emerging transformer-based models (12 implementations)-for classification, object detection, and segmentation tasks are systematically categorized and evaluated. The investigation encompasses hyperparameter optimization techniques utilized to enhance model performance, with particular focus on genetic algorithms and particle swarm optimization approaches. The role of explainable AI methods in medical diagnosis interpretation is analyzed through visualization techniques such as Grad-CAM and SHAP. Technical limitations, including dataset scarcity, computational constraints, and standardization challenges, are identified through trend analysis. Research gaps in current methodologies are highlighted through comparative assessment of performance metrics across different architectural implementations. Potential future research directions, including multimodal learning and federated learning approaches, are proposed based on publication trend analysis. This review serves as a comprehensive reference for researchers in medical image analysis and clinical practitioners implementing AI-based colorectal cancer detection systems.

Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) remain key challenges in medical image analysis, largely due to the lack of high-quality, balanced, and diverse datasets. In this work, we present a new curated MRI dataset designed specifically for brain tumor segmentation and classification tasks. The dataset comprises 6,000 contrast-enhanced T1-weighted MRI scans annotated by certified radiologists and physicians, spanning three major tumor types-glioma, meningioma, and pituitary-as well as non-tumorous cases. Each sample includes high-resolution labels and is categorized across axial, sagittal, and coronal imaging planes to facilitate robust model development and cross-view generalization. To demonstrate the utility of the dataset, we propose a transformer-based segmentation model and benchmark it against established baselines. Our method achieves the highest weighted mean Intersection-over-Union (IoU) of 82.3%, with improvements observed across all tumor categories. Importantly, this study serves primarily as an introduction to the dataset, establishing foundational benchmarks for future research. We envision this dataset as a valuable resource for advancing machine learning applications in neuro-oncology, supporting both academic research and clinical decision-support development. datasetlink: https://www.kaggle.com/datasets/briscdataset/brisc2025/

Accurate detection of cortical infarct is critical for timely treatment and improved patient outcomes. Current brain imaging methods often require invasive procedures that primarily assess blood vessel and structural white matter damage. There is a need for non-invasive approaches, such as functional MRI (fMRI), that better reflect neuronal viability. This study utilized automated machine learning (auto-ML) techniques to identify novel infarct-specific fMRI biomarkers specifically related to chronic cortical infarcts. We analyzed resting-state fMRI data from the multi-center ADNI dataset, which included 20 chronic infarct patients and 30 cognitively normal (CN) controls. This study utilized automated machine learning (auto-ML) techniques to identify novel fMRI biomarkers specifically related to chronic cortical infarcts. Surface-based registration methods were applied to minimize partial-volume effects typically associated with lower resolution fMRI data. We evaluated the performance of 7 previously known fMRI biomarkers alongside 107 new auto-generated fMRI biomarkers across 33 different classification models. Our analysis identified 6 new fMRI biomarkers that substantially improved infarct detection performance compared to previously established metrics. The best-performing combination of biomarkers and classifiers achieved a cross-validation ROC score of 0.791, closely matching the accuracy of diffusion-weighted imaging methods used in acute stroke detection. Our proposed auto-ML fMRI infarct-detection technique demonstrated robustness across diverse imaging sites and scanner types, highlighting the potential of automated feature extraction to significantly enhance non-invasive infarct detection.

The integration of multi-modal Magnetic Resonance Imaging (MRI) and clinical data holds great promise for enhancing the diagnosis of neurological disorders (NDs) in real-world clinical settings. Deep Learning (DL) has recently emerged as a powerful tool for extracting meaningful patterns from medical data to aid in diagnosis. However, existing DL approaches struggle to effectively leverage multi-modal MRI and clinical data, leading to suboptimal performance. To address this challenge, we utilize a unique, proprietary multi-modal clinical dataset curated for ND research. Based on this dataset, we propose a novel transformer-based Mixture-of-Experts (MoE) framework for ND classification, leveraging multiple MRI modalities-anatomical (aMRI), Diffusion Tensor Imaging (DTI), and functional (fMRI)-alongside clinical assessments. Our framework employs transformer encoders to capture spatial relationships within volumetric MRI data while utilizing modality-specific experts for targeted feature extraction. A gating mechanism with adaptive fusion dynamically integrates expert outputs, ensuring optimal predictive performance. Comprehensive experiments and comparisons with multiple baselines demonstrate that our multi-modal approach significantly enhances diagnostic accuracy, particularly in distinguishing overlapping disease states. Our framework achieves a validation accuracy of 82.47\%, outperforming baseline methods by over 10\%, highlighting its potential to improve ND diagnosis by applying multi-modal learning to real-world clinical data.