Intelligence-based medicine最新文献

{"title":"DSCBAM-Net: A dual-attention deep learning framework for retinal vessel segmentation and feature-driven vascular analysis","authors":"Rajatha ,&nbsp;D.V. Ashoka","doi":"10.1016/j.ibmed.2026.100370","DOIUrl":"10.1016/j.ibmed.2026.100370","url":null,"abstract":"<div><div>Retinal vessel segmentation is a crucial task in biomedical image analysis, enabling the understanding of vascular structures. Precise vessel segmentation reveals underlying vascular abnormalities in disease progression. Recent approaches have improved segmentation performance; however, maintaining computational efficiency while preserving accurate delineation of thin and low-contrast vessels remains an open engineering challenge. This study presents DSCBAM-Net, a lightweight dual-attention deep learning framework that integrates channel-wise and spatial attention mechanisms with multi-scale dilated convolution feature encoding to enhance vessel representation, particularly for thin and low-contrast vessel structures. The architecture follows a multi-stage design: (1) A robust encoder that extracts spatial and contextual features using standard and dilated convolutions enhanced with attention mechanisms. (2) A bottleneck module that fuses contextual features using parallel dilations. (3) A decoder with deep supervision for progressive vessel enhancement, enabling precise segmentation with fewer parameters and faster convergence. A composite hybrid loss function that integrates Dice, Focal-Tversky, and Top-k loss is introduced to address class imbalance and emphasize difficult vessel pixels. Following segmentation, various morphological features are extracted and used in a Retinal Vein Occlusion (RVO) detection and grading module, which estimates occlusion probability, stratifies the severity, and highlights dominant vascular drivers for explainability.</div><div>Extensive experiments were conducted on the merged dataset with an impressive dice score of 0.82. DSCBAM-Net also demonstrates superior cross-dataset performance, achieving a dice score of 0.879, 0.890, and 0.883 on DRIVE, STARE, and CHASE-DB1. In addition to segmentation, vessel-based structural features are extracted from the predicted masks to enable feature-driven vascular analysis and interpretability. Qualitative visualization, further highlights the effectiveness of the proposed architecture. Thus, DSCBAM-Net provides a robust and efficient solution for retinal vessel segmentation and downstream analytics tasks.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100370"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the role of synthetic data in the future of AI in healthcare: A scoping review of frameworks, challenges, and implications","authors":"Mohammad Ishtiaque Rahman ,&nbsp;Md Razuan Hossain ,&nbsp;S.M. Sayem ,&nbsp;Forhan Bin Emdad","doi":"10.1016/j.ibmed.2025.100342","DOIUrl":"10.1016/j.ibmed.2025.100342","url":null,"abstract":"<div><div>Synthetic data has emerged as a transformative tool in healthcare, particularly in areas such as medical imaging, electronic health records (EHRs), and clinical trial simulation, where data privacy, diversity, and accessibility are critical. This scoping review examines current approaches to synthetic data generation in healthcare, with a focus on AI model training, privacy preservation, and bias mitigation. A comprehensive search of PubMed, IEEE Xplore, and ACM Digital Library yielded 2906 studies, of which 42 met the inclusion criteria. Key data generation techniques included generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, Bayesian networks, federated learning, recurrent neural networks (RNNs), large language models (LLMs), agent-based models, graph-based generators, and SMOTE-based oversampling. Applications ranged from diagnostic model development to privacy-preserving data sharing and educational simulation. However, the field faces persistent challenges, including inconsistent validation practices, the absence of standard benchmarks, high computational demands, and ethical concerns related to consent and bias. This review underscores the need for standardized evaluation protocols, clearer regulatory guidance, and multidisciplinary collaboration to ensure the safe, equitable, and effective use of synthetic data in healthcare AI. In addition to technical advances, the review highlights the socio-technical implications of synthetic data adoption, including its impact on health equity, patient trust, and clinical decision-making.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100342"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Securing patient-specific ECG data in telemedicine through adaptive wavelet-based watermarking","authors":"Rania Hamami ,&nbsp;Narima Zermi ,&nbsp;Larbi Boubchir ,&nbsp;Amine Khaldi ,&nbsp;Med Redouane Kafi ,&nbsp;Aditya Kumar Sahu ,&nbsp;Narimene Mimoune","doi":"10.1016/j.ibmed.2026.100357","DOIUrl":"10.1016/j.ibmed.2026.100357","url":null,"abstract":"<div><div>Watermarking proves to be an effective technique for safeguarding crucial medical information. In this research, we propose a robust and imperceptible watermarking method designed to enhance the security of telemedicine-transmitted medical electrocardiogram (ECG) data. Embedding a mark in medical ECGs enables precise patient identification, reduces the risk of confusion during scans, and helps prevent diagnostic errors that could have adverse consequences. To ensure the security of ECG signals exchanged in telemedicine, our approach involves a frequency-domain watermarking method that conceals electronic patient records within the corresponding ECG signals. In this methodology, the signal undergoes a conversion into a 2D image, followed by a three-layer transform to extract the frequency content of the medical image. The low-frequency subbands undergo Schur decomposition, and the watermark bits are subsequently incorporated into the values of the upper triangular matrix. According to experimental results, these proposed techniques maintain a significant level of watermarked ECG quality while demonstrating high resistance to standard attacks. Experimental results show that the proposed SWT–Schur-based watermarking scheme achieves an average PSNR of 44.56 dB and an NCC higher than 0.95 under most common signal processing attacks. The average embedding capacity is 0.27 bits per pixel (BPP), while preserving the diagnostic quality of the ECG signals.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100357"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A smart system for accurate detection and classification of cardio vascular diseases using advanced analysis","authors":"Sapana Bhushan Raghuwanshi,&nbsp;Nilesh Ashok Suryawanshi","doi":"10.1016/j.ibmed.2026.100351","DOIUrl":"10.1016/j.ibmed.2026.100351","url":null,"abstract":"<div><div>Cardiovascular diseases (CVDs) are the leading cause of global mortality, highlighting the need for accurate and early diagnosis to enhance patient outcomes. However, effectively detecting and classifying CVDs using ECG signals remains a critical challenge. Hence, a novel <strong>Adaptive Temporal Wavelet-Kalman Fusion Transformer Network</strong> is proposed for enhanced CVD diagnosis. Initially, existing models struggle to capture nonlinear ventricular repolarization dynamics and subtle T-wave heterogeneity, leading to incomplete feature extraction. So, a novel Temporal Sparse Wavelet-Fourier Attention Transformer integrates Sparse Wavelet-Fourier Transform Decomposition to extract transient ECG variations, enhancing feature extraction. In contrast, Temporal Convolutional Transformers (TCTs) captures temporal dependencies for improved detection. Besides, existing ECG-based models fail to distinguish autonomic dysfunction (AD)-induced variations from pathological abnormalities, leading to misclassifications, and delayed diagnoses in CVD detection. Thus, an Adaptive Temporal Kalman Deep Fusion Forest Network integrates Adaptive Hybrid Empirical Kalman Decomposition to filter autonomic noise. At the same time, the Temporal Residual Gated Fusion Forest Network extracts spatial features, enhancing classification robustness. Further, the framework is validated using Stratified K-Fold Cross-Validation to ensure fairness and minimize bias, with experimental results demonstrating high accuracy, precision, and low MSE, leading to improved detection and classification of CVD.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100351"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning vision–language model for diagnosing pediatric dental diseases","authors":"Tuan D. Pham","doi":"10.1016/j.ibmed.2026.100364","DOIUrl":"10.1016/j.ibmed.2026.100364","url":null,"abstract":"<div><h3>Background:</h3><div>Automated diagnosis of pediatric dental diseases from panoramic radiographs remains challenging due to anatomical variability and limited availability of specialist expertise. Vision–language models offer a potential approach by integrating visual and textual information to improve diagnostic performance and interpretability.</div></div><div><h3>Objective:</h3><div>To develop and evaluate a deep learning vision–language model for differentiating between caries and periapical infections in pediatric panoramic radiographs.</div></div><div><h3>Methods:</h3><div>A multimodal framework was proposed that combines visual features extracted from panoramic radiographs using non-linear dynamics and textural encoding with textual descriptions generated by a large language model. The fused multimodal representations were used to train a one-dimensional convolutional neural network classifier. Model performance was evaluated using accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC).</div></div><div><h3>Results:</h3><div>Experiments conducted on a small, single-center dataset demonstrated that the proposed model outperformed conventional image-only convolutional neural networks and standalone language-based approaches, achieving an accuracy of 90%, sensitivity of 92%, specificity of 83%, precision of 92%, F1 score of 0.90, and an AUC of 0.96 within this dataset. However, the limited sample size and absence of external or prospective clinical validation restrict the generalizability and immediate clinical applicability of these findings.</div></div><div><h3>Conclusions:</h3><div>The results suggest that integrating visual and textual representations can enhance diagnostic performance for pediatric dental disease classification. Nevertheless, the findings should be regarded as preliminary and hypothesis-generating. Future work will involve larger, multi-center studies, external validation, and prospective clinical evaluation to establish robustness, generalizability, and real-world clinical impact of vision–language models in pediatric dental diagnostics.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100364"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CCSA-RF enhanced lightweight multi-scale CNN framework for robust lung cancer classification","authors":"R. Gayathri,&nbsp;R. Thilagavathy","doi":"10.1016/j.ibmed.2026.100363","DOIUrl":"10.1016/j.ibmed.2026.100363","url":null,"abstract":"<div><div>The early and accurate diagnosis of lung cancer is an urgent issue owing to the noise of imaging, nonhomogeneous nodule structures, and high cost of computing the current deep learning architectures. Most modern methods find it difficult to strike a balance between diagnostic accuracy, strength, and efficiency, especially with multimodal medical images. To overcome these shortcomings, this study proposes a single lightweight-based learning framework that combines preprocessing using wavelets, feature extraction under attention, chaos-based feature selection, and multiscale convolutional learning. First, a Ricker wavelet invariant center-weighted mean (RWICWM) filter was used to reduce noise without unimportant structures in the diagnostics. An Attention-Based DenseNet (ATT-DenseNet) is then used to extract discriminative features by highlighting the parts of the image that contain cancer-related features with channel-wise attention. The chaotic convolutional spectral analysis random forest (CCSA-RF) mechanism was proposed to select informative features and eliminate redundancy. Finally, a lightweight multi-scale CNN (LMS-CNN) is employed to achieve effective and precise classification of lung cancer. On the LC25000 and LIDC-IDRI datasets, the proposed framework attained an accuracy of 96.54, recall of 96.79, precision of 96.90, and F1-score of 96.32, with both datasets reporting similar results. The findings indicate that the proposed method enhances the classification accuracy and strength and still has a low computational complexity, which renders it applicable for real-world clinical use. The proposed research offers a computationally and theoretically sound solution that sets the state-of-the-art in lung cancer diagnostics.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100363"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization framework for overcoming tyrosine kinase inhibitor resistance: Multi-objective selection, scheduling, and adaptive therapy","authors":"Mohanad A. Deif ,&nbsp;Mohamed A. Hafez ,&nbsp;Mohammad Khishe","doi":"10.1016/j.ibmed.2025.100332","DOIUrl":"10.1016/j.ibmed.2025.100332","url":null,"abstract":"<div><div>Tyrosine kinase inhibitors are key drugs in targeted cancer therapy but often fail when resistance emerges. Many predictive methods focus on accuracy alone, while calibration and kinase selectivity, which matter for clinical use, receive less attention. We present a single framework that treats resistance prediction and dosing decisions as a three-objective problem: minimize misclassification, reduce calibration error, and increase selectivity. Using calibrated probabilities and tuned thresholds, baseline models improved in ROC–AUC and expected calibration error across stratified, scaffold, and mutation-cold splits. Pareto analysis with hypervolume and coverage showed that including selectivity changes the relative ranking of inhibitors and exposes trade-offs that accuracy alone cannot capture. On the treatment side, we compared continuous dosing, hysteresis switching, and adaptive model predictive control in a two-compartment tumor model. Adaptive control lowered total dose by about 18% and extended simulated survival by more than 25 weeks. These results provide a clear proof of concept that combining machine learning, multi-objective optimization, and adaptive therapy can improve prediction quality and guide personalized dosing to better manage resistance.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100332"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LIO-VisionAR: Intelligence-enabled augmented reality guidance for laser indirect ophthalmoscope-based retinal laser therapy","authors":"Sangjun Eom ,&nbsp;Tiffany Ma ,&nbsp;Miroslav Pajic ,&nbsp;Maria Gorlatova ,&nbsp;Majda Hadziahmetovic","doi":"10.1016/j.ibmed.2026.100353","DOIUrl":"10.1016/j.ibmed.2026.100353","url":null,"abstract":"<div><h3>Objective</h3><div>Laser indirect ophthalmoscope (LIO) retinal therapy is a complex procedure that demands precision. We present LIO-VisionAR, an intelligence-enabled augmented reality (AR) guidance system designed to support safer and more effective training for LIO-based retinal laser therapy.</div></div><div><h3>Methods</h3><div>A custom retina model with retinopathy areas was developed and integrated into a human phantom model. A virtual retina model and simulator were developed using the color fundus photo to compute the magnification and laser targeting guidance based on the user's AR head-mounted device movement. Randomized user trials compared conventional and AR-guided retinal laser tasks, while multimodal behavioral telemetry were recorded for quantitative performance analysis and proof-of-concept skill inference.</div></div><div><h3>Results</h3><div>A total of 11 experts and 12 non-experts were included in the study. With AR guidance, laser targeting accuracy increased from 70.8 % to 82.6 % for experts and from 65.7 % to 81.7 % for non-experts. AR guidance increased laser instrumentation time, reflecting a deliberate speed–accuracy trade-off. Analysis of AR-captured behavioral telemetry showed that gaze exploration and temporal control features were associated with performance, and unsupervised clustering revealed distinct behavioral strategies linked to progressively higher accuracy. A composite performance-based skill score exhibited a moderate positive association with laser accuracy (Spearman ρ = 0.45, p = 0.032). Over 80 % of experts agreed that our system is appropriate for teaching and could improve retinal laser therapy training and safety.</div></div><div><h3>Conclusions</h3><div>LIO-VisionAR improves procedural accuracy under simulated conditions and demonstrates a concrete pathway toward adaptive, intelligence-based AR guidance for ophthalmic microsurgical training.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100353"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using convolutional network in graphical model detection of autism disorders with fuzzy inference systems","authors":"S. Rajaprakash ,&nbsp;C. Bagath Basha ,&nbsp;C. Sunitha Ram ,&nbsp;I. Ameethbasha ,&nbsp;V. Subapriya ,&nbsp;R. Sofia","doi":"10.1016/j.ibmed.2025.100213","DOIUrl":"10.1016/j.ibmed.2025.100213","url":null,"abstract":"<div><div>Autism spectrum disorder (ASD) study faces several challenges, including variations in brain connectivity patterns, small sample sizes, and data heterogeneity detection by magnetic resonance imaging (MRI). These issues make it challenging to identify consistent imaging modalities. Researchers have explored improved analysis techniques to solve the above problem via multimodal imaging and graph-based methods. Therefore, it is better to understand ASD neurology. The current techniques focus mainly on pairwise comparisons between individuals and often overlook features and individual characteristics. To overcome these limitations, in the proposed novel method, a multiscale enhanced graph with a convolutional network is used for ASD detection.</div><div>This work integrates non-imaging phenotypic data (from brain imaging data) with functional connectivity data (from Functional magnetic resonance images). In this approach, the population graph represents all individuals as vertices. The phenotypic data were used to calculate the weight between vertices in the graph using the fuzzy inference system. Fuzzy if-then rules, is used to determine the similarity between the phenotypic data. Each vertice connects feature vectors derived from the image data. The vertices and weights of each edge are used to incorporate phenotypic information. A random walk with a fuzzy MSE-GCN framework employs multiple parallel GCN layer embeddings. The outputs from these layers are joined in a completely linked layer to detect ASD efficiently. We assessed the performance of this background by the ABIDE data set and utilized recursive feature elimination and a multilayer perceptron for feature selection. This method achieved an accuracy rate of 87 % better than the current study.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100213"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-silo federated learning vs. centralized learning for segmenting acute and chronic ischemic brain lesions","authors":"Joon Kim ,&nbsp;Hoyeon Lee ,&nbsp;Jonghyeok Park ,&nbsp;Sang Hyun Park ,&nbsp;Myungjae Lee ,&nbsp;Leonard Sunwoo ,&nbsp;Chi Kyung Kim ,&nbsp;Beom Joon Kim ,&nbsp;Dong-Eog Kim ,&nbsp;Wi-Sun Ryu","doi":"10.1016/j.ibmed.2025.100283","DOIUrl":"10.1016/j.ibmed.2025.100283","url":null,"abstract":"<div><h3>Objectives</h3><div>To investigate the efficacy of federated learning (FL) compared to industry-level centralized learning (CL) for segmenting acute infarct and white matter hyperintensity.</div></div><div><h3>Materials and methods</h3><div>This retrospective study included 13,546 diffusion-weighted images (DWI) from 10 hospitals and 8421 fluid-attenuated inversion recovery (FLAIR) images from 9 hospitals for acute (Task I) and chronic (Task II) lesion segmentation. We trained with datasets originated from 9 and 3 institutions for Task I and Task II, respectively, and externally tested them in datasets originated from 1 and 6 institutions each. For FL, the central server aggregated training results every four rounds with FedYogi (Task I) and FedAvg (Task II). A batch clipping strategy was tested for the FL models. Performances were evaluated with the Dice similarity coefficient (DSC).</div></div><div><h3>Results</h3><div>The mean ages (SD) for the training datasets were 68.1 (12.8) for Task I and 67.4 (13.0) for Task II. The frequency of male participants was 51.5 % and 60.4 %, respectively. In Task I, the FL model employing batch clipping trained for 360 epochs achieved a DSC of 0.754 ± 0.183, surpassing an equivalently trained CL model (DSC 0.691 ± 0.229; p &lt; 0.001) and comparable to the best-performing CL model at 940 epochs (DSC 0.755 ± 0.207; p = 0.701). In Task II, no significant differences were observed amongst FL model with clipping, without clipping, and CL model after 48 epochs (DSCs of 0.761 ± 0.299, 0.751 ± 0.304, 0.744 ± 0.304). Few-shot FL showed significantly lower performance. Task II reduced training times with batch clipping (3.5–1.75 h).</div></div><div><h3>Conclusions</h3><div>Comparisons between CL and FL in identical settings suggest the feasibility of FL for medical image segmentation.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100283"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}