Journal of imaging informatics in medicine最新文献

{"title":"Generative AI-Based Data Augmentation for Oral Lesion Classification: The PhotoMOCI Dataset and Benchmark.","authors":"Marco Parola, Mario G C A Cimino, Sabrina Senatore","doi":"10.1007/s10278-026-02373-5","DOIUrl":"https://doi.org/10.1007/s10278-026-02373-5","url":null,"abstract":"<p><p>Early detection of oral cancer via photographic imaging presents a promising avenue for large-scale oral cavity screening. However, the development of robust deep learning models is frequently hampered by the scarcity of high-quality, annotated datasets. To address this limitation, a novel and well-curated resource, the Photographic Multi-purpose Oral Cancer Imaging (PhotoMOCI) dataset, is introduced for developing models across multiple diagnostic tasks in oral oncology. Then, a comprehensive benchmark study was conducted to investigate how various data augmentation strategies influence the performance of image classifiers. Our analysis spans different generative AI frameworks, evaluating the efficacy of traditional methods against advanced generative approaches, including generative adversarial networks (GANs) and diffusion models (DMs). Additionally, we propose the Synthetic Image Filter (SIF), a mechanism to select specific samples based on two auxiliary models: Synthetic Proxy Classifier to ensure samples are representative of the target class and Synthetic Image Detector to verify they appear realistic, thereby selecting only the high-utility images that contribute to improving downstream performance. Across the evaluated datasets and classifiers, the best SIF-filtered setup improves accuracy over traditional augmentation in all cases, with gains of +1.73% and +2.35% on PhotoMOCI and +2.38% and +2.08% on KOCD for ResNet50 and ViT, respectively. Our findings reveal that while the direct application of generative data augmentation may yield performance drops, the integration of SIF, considering (i) how synthetic data looks real and (ii) how it reflects the discriminative features of the belonging class, provides a simple yet effective mechanism to filter out synthetic samples that confuse the classifier during training.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149248428","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":"Application and Improvement of YOLO11 for Carotid Plaque Detection in Handheld Ultrasound Images.","authors":"Yinghui Zhu, Hang Qu, Yi Zhao","doi":"10.1007/s10278-026-02426-9","DOIUrl":"https://doi.org/10.1007/s10278-026-02426-9","url":null,"abstract":"<p><p>Early and accurate detection of carotid plaques is clinically significant for stroke risk assessment and primary care screening. To address challenges such as low resolution, small target size, and blurred boundaries in handheld ultrasound images of carotid plaques, this paper proposes an improved YOLO11n model named YOLO11-MGD. The model introduces the C2PSA_Mona module to suppress interference from normal anatomical structures and incorporates the C3k2_GCConv module to enhance global context modeling capabilities, thereby alleviating semantic fragmentation caused by heterogeneous plaques. Additionally, it integrates a Dynamic Head to achieve joint dynamic attention allocation across scale, space, and task dimensions, and it designs the Inner_MPDIoU loss function to improve bounding box regression accuracy for irregular plaques. Experimental results show that on a self-constructed dataset containing 2078 carotid plaque images, YOLO11-MGD achieves a mean mAP50 of 93.16%, outperforming several mainstream object detection models. While maintaining its lightweight advantage, the proposed method significantly enhances the accuracy and robustness of carotid plaque detection in low-quality ultrasound images and demonstrates good application value for primary care stroke risk screening.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149248354","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":"Field-Strength Dependence of Automated Subcortical Volumetry: A Paired 3 T-7 T Benchmarking Study of BrainSuite, FastSurfer and NextBrain.","authors":"Emre Emekli, Burak Can Demirel, Esra Emekli","doi":"10.1007/s10278-026-02422-z","DOIUrl":"https://doi.org/10.1007/s10278-026-02422-z","url":null,"abstract":"<p><p>Automated segmentation pipelines are widely used for quantitative assessment of subcortical brain structures, yet it remains unclear whether measurements obtained at different magnetic field strengths are numerically interchangeable. This study evaluated the cross-field agreement and inter-software consistency of automated subcortical volumetry using paired 3T and 7T structural MRI data from 30 healthy adults. T1-weighted images were independently processed using BrainSuite, FastSurfer and NextBrain. Volumes of the bilateral putamen, globus pallidus, nucleus accumbens, thalamus and caudate nucleus were analysed. Within-software agreement between 3 and 7T measurements was assessed using two-way mixed-effects, single-measure intraclass correlation coefficients for absolute agreement. Inter-software agreement was examined separately at 3T and 7T using both absolute agreement and consistency intraclass correlation coefficients. All within-software regional comparisons and selected inter-software comparisons were further evaluated using Bland-Altman analyses. Cross-field agreement varied markedly across software packages and anatomical regions. BrainSuite showed poor absolute agreement across all regions, whereas FastSurfer and NextBrain demonstrated moderate-to-good agreement in selected structures. Inter-software consistency was generally higher than absolute agreement, indicating preservation of relative participant ranking despite systematic differences in measured volumes. Segmentation failures were more frequent at 7T, particularly for BrainSuite. Bland-Altman analyses confirmed software- and region-specific systematic differences and wide limits of agreement. These findings indicate that the evaluated automated segmentation pipelines do not provide field-strength-invariant subcortical volume estimates. Measurements obtained at 3T and 7T, or using different software packages, should not be considered directly interchangeable without pipeline-specific validation or harmonisation.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149248384","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":"Clinical CT Data Infrastructure Provides Partial Support for an Operational Ontology for Radiology.","authors":"Njood Alsaihati, Yunrong Cai, Jered Wells, Justin Solomon, Donald P Frush, Ehsan Samei","doi":"10.1007/s10278-026-02382-4","DOIUrl":"https://doi.org/10.1007/s10278-026-02382-4","url":null,"abstract":"<p><p>Medical imaging data are increasingly used beyond patient care to support research, quality assessment, and artificial intelligence development. These applications require imaging examinations to be represented in ways that enable consistent aggregation across clinical operations to ensure reliable interpretation. In prior work, we introduced an ontology framework, the Operational Ontology for Radiology (OOR), designed to represent the radiology imaging workflow for computed tomography (CT) examinations through entities and relationships. This investigation evaluates the extent to which existing clinical imaging infrastructure can support the implementation of this framework. Using OOR, 156 data properties representing components of the imaging workflow were assessed within the clinical imaging environment at one academic medical center. Each property was classified according to its availability in institutional data systems into four categories: directly available and standardized, indirectly available and standardized, available but not standardized, or not readily available. The distribution of properties across these categories was quantified overall and across ontology classes. Among the evaluated properties, 48.1% were directly available in structured form, 7.1% were indirectly available and standardized, 33.3% required standardization, and 11.5% were not readily available in the systems. Technical acquisition parameters, device information, and patient attributes were most frequently available, while elements related to imaging tasks, contextual workflow attributes, and certain series-level descriptors required standardization or structured representation. These findings demonstrate that a portion of OOR can be supported using clinical data infrastructures, while also identifying areas where improved standardization and structured data representation are needed to enable large-scale analysis of clinical imaging data.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149241120","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":"Computer-Aided Diagnosis of Clinically Significant Prostate Cancer in Transrectal Ultrasound.","authors":"Zetian Feng, Juan Fu, Xuebin Zou, Hongsheng Ye, Haoyu Pan, Yong-Hong Li, Jianhua Zhou, Yi Wang","doi":"10.1007/s10278-026-02409-w","DOIUrl":"https://doi.org/10.1007/s10278-026-02409-w","url":null,"abstract":"<p><p>Diagnosing clinically significant prostate cancer (csPCa) using transrectal ultrasound (TRUS) remains a challenging task due to limited image contrast and the subtle appearance of malignant lesions. In this study, we propose a novel hybrid framework that integrates multi-modality and multi-task learning for csPCa classification from TRUS data. The framework leverages complementary information from B-mode and strain elastography (SE) imaging via a dual-stream encoder, enhanced by cross-modal attention gate (CMAG) modules at each level. These CMAG modules first adaptively integrate cross-modal features and then employ a gating mechanism to regulate the flow of cross-modal context into each modality-specific stream. To mitigate the scarcity of segmentation annotations, we introduce a multi-scale prototype-guided learning (MSPL) strategy, which propagates supervision signals from a small set of labeled samples to unlabeled ones by leveraging learned prototypes across multiple feature scales. This design allows the task-shared encoder to benefit from structural cues captured by the segmentation branch, even with minimal voxel-level supervision. Additionally, a context-aware multi-scale classifier (CMSC) aggregates hierarchical decoder features to further exploit the synergy between classification and segmentation tasks. The proposed method is evaluated on an in-house dataset containing 612 subjects who underwent prostate biopsy and attains favorable performance in identifying csPCa with an area under the curve (AUC) of 0.86. Extensive comparative and ablation results further highlight the efficacy of incorporating cross-modal interaction, prototype-guided learning, and context-aware classification for accurate and annotation-efficient csPCa diagnosis. The code is publicly available at https://github.com/mock1ngbrd/ProstateMMTNet .</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149241125","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":"Toward Verifiable Medical Imaging World Models: An Engineering Framework for Evaluating Simulation Fidelity, Roll-Out Stability, and Clinical Plausibility.","authors":"Chathura Wimalasiri","doi":"10.1007/s10278-026-02413-0","DOIUrl":"https://doi.org/10.1007/s10278-026-02413-0","url":null,"abstract":"<p><p>Medical world models simulate how a patient's imaged state evolves over time and, in some systems, under clinical interventions. Their value comes from multi-step, action-conditioned roll-outs, yet evaluation is still dominated by perceptual or single-step metrics. We treat assessment of these models as a verification problem, distinct from later evidence of clinical benefit, and organize it around three axes. Simulation fidelity asks whether predicted states are correct at the level claimed by the model. Roll-out stability asks whether that correctness is maintained over multiple steps; we summarize this using an effective planning horizon, defined as the last step before a stated loss quantile first exceeds a clinical tolerance under the observed action policy. Clinical plausibility and reliability ask whether simulated responses are counterfactually consistent, calibrated, and adequate across patient subgroups. We also propose a claim-proportionate reporting standard and a reproducible procedure for estimating the horizon from retrospective data. Applying the framework to a purposively selected sample of nine imaging world models, we find that reported evidence is uneven across capability tiers. Under the proposed standard, every model claiming action-conditioned prediction or above omits at least one applicable item: none reports an explicit counterfactual-consistency check or a clinically meaningful subgroup analysis of the type specified in Item 7, and none reports an effective planning horizon at a stated clinical tolerance. Because the effective planning horizon is estimated on-policy, it should not be interpreted as evidence of off-policy counterfactual reliability.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149241095","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":"TRACE-CLDN: An Integrated Computational Pathology Framework for Tumor-Restricted Quantification of CLDN18.2 Expression Burden from H&E Whole-Slide Images in Intrahepatic Cholangiocarcinoma.","authors":"Yu-Meng Pan, Hao-Tian Zhang, Meng-Ling Liu, Xin-Yi Jiang, Zhi-Qiang Yang, Li Sui","doi":"10.1007/s10278-026-02433-w","DOIUrl":"https://doi.org/10.1007/s10278-026-02433-w","url":null,"abstract":"<p><p>Claudin-18.2 (CLDN18.2) is a membrane-associated biomarker with emerging translational relevance in biliary tract cancers. In intrahepatic cholangiocarcinoma (ICC), whole-slide image (WSI)-level assessment is complicated by abundant stroma, dispersed tumor glands, non-neoplastic epithelial structures, and heterogeneous membranous staining. We developed TRACE-CLDN (Tumor-Restricted Analysis for Computational Estimation of CLDN18.2), an integrated computational pathology framework for tumor-restricted quantification of CLDN18.2 expression burden from hematoxylin and eosin (H&E) WSIs. This retrospective study included 135 ICC cases with paired H&E and CLDN18.2 immunohistochemistry (IHC) WSIs. After cross-modal registration, IHC-derived annotations were mapped to H&E images to generate patch-level training data. TRACE-CLDN integrates two independently trained H&E-based modules for tumor region identification and CLDN18.2 expression label prediction, followed by WSI-level tumor-restricted aggregation. Agreement with pathologist consensus was assessed in a 45-case pathologist-validation subset, and exploratory survival analysis was performed in the full cohort. ROC-AUCs were 0.9968 ± 0.0009 for tumor identification and 0.9633 ± 0.0174 for expression prediction. The TRACE-CLDN-derived Ratio showed strong agreement with pathologist assessment (Spearman ρ = 0.876; weighted κ = 0.767). In exploratory survival analysis, the log-rank P value was 0.010 and the concordance index was 0.668. TRACE-CLDN enables tumor-restricted, continuous assessment of CLDN18.2 expression burden from H&E WSIs, with strong agreement with pathologist assessment and an exploratory association with survival in ICC.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149241107","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":"Operational Ontology for Radiology: A Framework for Profiling and Characterizing CT Imaging Data.","authors":"Njood Alsaihati, Yunrong Cai, Jered Wells, Justin Solomon, Donald P Frush, Ehsan Samei","doi":"10.1007/s10278-026-02370-8","DOIUrl":"https://doi.org/10.1007/s10278-026-02370-8","url":null,"abstract":"<p><p>Medical imaging data are increasingly used for benchmarking, quality improvement, research, and artificial intelligence applications. However, the lack of consistent metadata characterization limits the reliability and scalability of such analyses. Existing standards provide syntactic interoperability but often fail to capture the semantic clinical context and workflow details necessary for secondary uses such as AI development and radiation safety assessment, affecting the utility for reproducible profiling of imaging examinations across institutions. In this work, we describe key gaps in current approaches to imaging data standardization. These include syntactic interoperability without semantic equivalence, limited representation of clinically relevant context, inconsistent definitions of radiology procedures across institutions, and the absence of mechanisms to systematically profile imaging examinations for analysis. These limitations make it difficult to compare imaging practices, aggregate datasets, and interpret results across sites. To address these challenges, we have devised an ontology framework, the Operational Ontology for Radiology (OOR), that models the radiology study workflow from ordering through interpretation using explicitly defined entities, relationships, and annotated data properties. A CT-focused implementation of the framework was then formed, including 34 classes, 15 object properties, and 156 data properties in this present formation. The framework was deployed in a demonstrative use case scenario to illustrate the utility of OOR in drawing more clinically relevant radiation dose aggregates from clinical CT data. The work demonstrates that OOR provides both a structured representation of imaging data and a foundation for more transparent data aggregation, scalable secondary analysis, and reproducible characterization of imaging practices across institutions.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229681","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":"M4DTrack: A DICOM-Based Imaging Informatics Workflow for User-Supervised 4DCT Lung Tumor Motion Reporting.","authors":"Tae Kyu Lee","doi":"10.1007/s10278-026-02435-8","DOIUrl":"https://doi.org/10.1007/s10278-026-02435-8","url":null,"abstract":"<p><p>Four-dimensional computed tomography (4DCT) is routinely used in radiotherapy simulation to evaluate respiration-induced tumor motion, but the motion information is often reviewed visually rather than captured as structured, phase-resolved data. We developed and evaluated M4DTrack, a DICOM-based imaging-informatics workflow for user-supervised 4DCT lung tumor motion analysis and structured, exportable three-dimensional center-of-mass (CoM) motion reporting. M4DTrack imports phase-resolved DICOM CT images, groups images by respiratory phase, displays linked axial/coronal/sagittal views, supports crosshair-based tumor localization and HU inspection, and applies seed or linked 3D-box initialization with sequential phase-to-phase HU-volume propagation. An 8-case lower-lobe lung 4DCT dataset (80 respiratory phases) was analyzed. Directional amplitudes, 3D vector amplitude, maximum phase-wise displacement, and agreement with independent ITV-GTV reference amplitudes were quantified. HU-weighted CoM amplitudes were 2.44 ± 1.44 mm, 2.64 ± 1.37 mm, and 6.04 ± 2.48 mm in X, Y, and Z patient-coordinate directions, respectively. Mean 3D vector amplitude was 7.26 ± 2.50 mm, and mean maximum phase-wise 3D displacement was 6.20 ± 2.10 mm. Biases versus ITV-GTV reference amplitudes were 0.01, - 0.84, and 0.23 mm for X, Y, and Z, respectively, with a 3D vector amplitude bias of - 0.29 mm. M4DTrack provides a user-supervised imaging-informatics bridge between qualitative 4DCT cine review and structured quantitative motion documentation. The pilot results support feasibility for generating exportable, phase-resolved tumor-motion metrics while preserving user-supervised review of 4DCT artifacts, HU-capture uncertainty, adjacent anatomy, and differences between CoM-based and extent-based measurements.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229668","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":"TurkRadLLM: A Compact MedGemma-Based Model for Structuring Turkish Lumbar and Cervical Spine MRI Reports-A Multi-reader Validation Study.","authors":"Ahmet Harun Turgan, Hasan Çolakoğlu, Akif Demir, Zakir Sakcı","doi":"10.1007/s10278-026-02427-8","DOIUrl":"https://doi.org/10.1007/s10278-026-02427-8","url":null,"abstract":"<p><p>To develop and validate a compact, locally deployable MedGemma-based model converting unstructured Turkish spine MRI reports into structured six-field output. In this retrospective single-institution study, 5210 lumbar and 5017 cervical Turkish spine MRI reports were used to fine-tune MedGemma 4B IT with QLoRA. Silver-standard labels from a proprietary large language model served as training targets and as the reference for automated fidelity analyses, but not as the clinical reference standard. Evaluation comprised multi-reader validation of 180 authentic reports interleaved with 20 adversarial cases, a second round in which the readers scored the silver labels themselves, and comparison on the full held-out set against a rule-based baseline, the non-fine-tuned base model, and a much larger model. Every output was valid, schema-conformant JSON, and the grouped acceptance rate was 100% (95% CI 97.9-100), though one reader judged negative-finding lists incomplete in 57.8% of reports. On the full held-out set (1025 reports), fine-tuning increased agreement with the silver-label impression from 17.7% to 67.1% and urgent-flag sensitivity against the silver reference from 18.8% to 62.5% (both p < .001); the larger non-fine-tuned model reached 27.5% and 50.0%, and a rule-based baseline 99.0% on body region. When the readers scored the silver labels themselves, the model was rated higher on negative findings and lower on the impression. Deployment required 14.75 GB of GPU memory on a consumer card. Fine-tuning a compact medical language model produced structured output rated acceptable by clinicians, though negative-finding completeness remained reader-dependent. The gain lay in content-level fidelity rather than output validity.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229740","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}