Pub Date : 2026-01-28DOI: 10.1016/j.media.2026.103965
Yi Zhang, Yulin Yan, Kun Wang, Muyu Cai, Yifei Xiang, Yan Guo, Puxun Tu, Tao Ying, Xiaojun Chen
{"title":"A navigation-guided 3D breast ultrasound scanning and reconstruction system for automated multi-lesion spatial localization and diagnosis","authors":"Yi Zhang, Yulin Yan, Kun Wang, Muyu Cai, Yifei Xiang, Yan Guo, Puxun Tu, Tao Ying, Xiaojun Chen","doi":"10.1016/j.media.2026.103965","DOIUrl":"https://doi.org/10.1016/j.media.2026.103965","url":null,"abstract":"","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"76 1","pages":"103965"},"PeriodicalIF":10.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1016/j.media.2026.103963
Wei-Lun Huang, Minghao Xue, Zhiyou Liu, Davood Tashayyod, Jun Kang, Amir Gandjbakhche, Misha Kazhdan, Mehran Armand
{"title":"Revisiting Lesion Tracking in 3D Total Body Photography","authors":"Wei-Lun Huang, Minghao Xue, Zhiyou Liu, Davood Tashayyod, Jun Kang, Amir Gandjbakhche, Misha Kazhdan, Mehran Armand","doi":"10.1016/j.media.2026.103963","DOIUrl":"https://doi.org/10.1016/j.media.2026.103963","url":null,"abstract":"","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"92 1","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1016/j.media.2026.103962
Jiejiang Yu, Yu Liu
In recent years, semi-supervised methods have attracted considerable attention in gland segmentation of histopathological images, as they can substantially reduce the annotation data burden for pathologists. The most widely adopted approach is the Mean-Teacher framework based on consistency regularization, which exploits unlabeled data information through consistency regularization constraints. However, due to the morphological complexity of glands in histopathological images, existing methods still suffer from confusion between glands and background, as well as gland adhesion. To address these challenges, we propose a semi-supervised gland segmentation method based on Density Perturbation and Feature Recalibration (DPFR). Specifically, we first design a normalized flow-based density estimator to effectively model the feature density distributions of glands, contours, and background. The gradient information of the estimator is then exploited to determine the descent direction in low-density regions, along which perturbations are applied to enhance feature discriminability. Furthermore, a contrastive-learning-based feature recalibration module is designed to alleviate inter-class distribution confusion, thereby improving gland-background separability and mitigating gland adhesion. Extensive experiments on three public gland segmentation datasets demonstrate that the proposed method consistently outperforms existing semi-supervised approaches, achieving state-of-the-art performance with a substantial margin. The code repository address is https://github.com/Methow0/DPFR.
{"title":"DPFR: Semi-supervised gland segmentation via density perturbation and feature recalibration","authors":"Jiejiang Yu, Yu Liu","doi":"10.1016/j.media.2026.103962","DOIUrl":"10.1016/j.media.2026.103962","url":null,"abstract":"<div><div>In recent years, semi-supervised methods have attracted considerable attention in gland segmentation of histopathological images, as they can substantially reduce the annotation data burden for pathologists. The most widely adopted approach is the Mean-Teacher framework based on consistency regularization, which exploits unlabeled data information through consistency regularization constraints. However, due to the morphological complexity of glands in histopathological images, existing methods still suffer from confusion between glands and background, as well as gland adhesion. To address these challenges, we propose a semi-supervised gland segmentation method based on Density Perturbation and Feature Recalibration (DPFR). Specifically, we first design a normalized flow-based density estimator to effectively model the feature density distributions of glands, contours, and background. The gradient information of the estimator is then exploited to determine the descent direction in low-density regions, along which perturbations are applied to enhance feature discriminability. Furthermore, a contrastive-learning-based feature recalibration module is designed to alleviate inter-class distribution confusion, thereby improving gland-background separability and mitigating gland adhesion. Extensive experiments on three public gland segmentation datasets demonstrate that the proposed method consistently outperforms existing semi-supervised approaches, achieving state-of-the-art performance with a substantial margin. The code repository address is <span><span>https://github.com/Methow0/DPFR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103962"},"PeriodicalIF":11.8,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1016/j.media.2026.103953
Jef Jonkers , Frank Coopman , Luc Duchateau , Glenn Van Wallendael , Sofie Van Hoecke
Automatic anatomical landmark localization in medical imaging requires not just accurate predictions but reliable uncertainty quantification for effective clinical decision support. Current uncertainty quantification approaches often fall short, particularly when combined with normality assumptions, systematically underestimating total predictive uncertainty. This paper introduces conformal prediction as a framework for reliable uncertainty quantification in anatomical landmark localization, addressing a critical gap in automatic landmark localization. We present two novel approaches guaranteeing finite-sample validity for multi-output prediction: multi-output regression-as-classification conformal prediction (M-R2CCP) and its variant multi-output regression to classification conformal prediction set to region (M-R2C2R). Unlike conventional methods that produce axis-aligned hyperrectangular or ellipsoidal regions, our approaches generate flexible, non-convex prediction regions that better capture the underlying uncertainty structure of landmark predictions. Through extensive empirical evaluation across multiple 2D and 3D datasets, we demonstrate that our methods consistently outperform existing multi-output conformal prediction approaches in both validity and efficiency. This work represents a significant advancement in reliable uncertainty estimation for anatomical landmark localization, providing clinicians with trustworthy confidence measures for their diagnoses. While developed for medical imaging, these methods show promise for broader applications in multi-output regression problems.
{"title":"Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal prediction","authors":"Jef Jonkers , Frank Coopman , Luc Duchateau , Glenn Van Wallendael , Sofie Van Hoecke","doi":"10.1016/j.media.2026.103953","DOIUrl":"10.1016/j.media.2026.103953","url":null,"abstract":"<div><div>Automatic anatomical landmark localization in medical imaging requires not just accurate predictions but reliable uncertainty quantification for effective clinical decision support. Current uncertainty quantification approaches often fall short, particularly when combined with normality assumptions, systematically underestimating total predictive uncertainty. This paper introduces conformal prediction as a framework for reliable uncertainty quantification in anatomical landmark localization, addressing a critical gap in automatic landmark localization. We present two novel approaches guaranteeing finite-sample validity for multi-output prediction: multi-output regression-as-classification conformal prediction (M-R2CCP) and its variant multi-output regression to classification conformal prediction set to region (M-R2C2R). Unlike conventional methods that produce axis-aligned hyperrectangular or ellipsoidal regions, our approaches generate flexible, non-convex prediction regions that better capture the underlying uncertainty structure of landmark predictions. Through extensive empirical evaluation across multiple 2D and 3D datasets, we demonstrate that our methods consistently outperform existing multi-output conformal prediction approaches in both validity and efficiency. This work represents a significant advancement in reliable uncertainty estimation for anatomical landmark localization, providing clinicians with trustworthy confidence measures for their diagnoses. While developed for medical imaging, these methods show promise for broader applications in multi-output regression problems.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103953"},"PeriodicalIF":11.8,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1016/j.media.2026.103964
Pablo Meseguer , Rocío del Amor , Valery Naranjo
Contrastive language-image pretraining has greatly enhanced visual representation learning and enabled zero-shot classification. Vision-language language models (VLM) have succeeded in few-shot learning by leveraging adaptation modules fine-tuned for specific downstream tasks. In computational pathology (CPath), accurate whole-slide image (WSI) prediction is crucial for aiding in cancer diagnosis, and multiple instance learning (MIL) remains essential for managing the gigapixel scale of WSIs. In the intersection between CPath and VLMs, the literature still lacks specific adapters that handle the particular complexity of the slides. To solve this gap, we introduce MIL-Adapter, a novel approach designed to obtain consistent slide-level classification under few-shot learning scenarios. In particular, our framework is the first to combine trainable MIL aggregation functions and lightweight visual-language adapters to improve the performance of histopathological VLMs. MIL-Adapter relies on textual ensemble learning to construct discriminative zero-shot prototypes. It is serves as a solid starting point, surpassing MIL models with randomly initialized classifiers in data-constrained settings. With our experimentation, we demonstrate the value of textual ensemble learning and the robust predictive performance of MIL-Adapter through diverse datasets and configurations of few-shot scenarios, while providing crucial insights on model interpretability. The code is publicly accessible in https://github.com/cvblab/MIL-Adapter.
{"title":"MIL-Adapter: Coupling multiple instance learning and vision-language adapters for few-shot slide-level classification","authors":"Pablo Meseguer , Rocío del Amor , Valery Naranjo","doi":"10.1016/j.media.2026.103964","DOIUrl":"10.1016/j.media.2026.103964","url":null,"abstract":"<div><div>Contrastive language-image pretraining has greatly enhanced visual representation learning and enabled zero-shot classification. Vision-language language models (VLM) have succeeded in few-shot learning by leveraging adaptation modules fine-tuned for specific downstream tasks. In computational pathology (CPath), accurate whole-slide image (WSI) prediction is crucial for aiding in cancer diagnosis, and multiple instance learning (MIL) remains essential for managing the gigapixel scale of WSIs. In the intersection between CPath and VLMs, the literature still lacks specific adapters that handle the particular complexity of the slides. To solve this gap, we introduce MIL-Adapter, a novel approach designed to obtain consistent slide-level classification under few-shot learning scenarios. In particular, our framework is the first to combine trainable MIL aggregation functions and lightweight visual-language adapters to improve the performance of histopathological VLMs. MIL-Adapter relies on textual ensemble learning to construct discriminative zero-shot prototypes. It is serves as a solid starting point, surpassing MIL models with randomly initialized classifiers in data-constrained settings. With our experimentation, we demonstrate the value of textual ensemble learning and the robust predictive performance of MIL-Adapter through diverse datasets and configurations of few-shot scenarios, while providing crucial insights on model interpretability. The code is publicly accessible in <span><span>https://github.com/cvblab/MIL-Adapter</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103964"},"PeriodicalIF":11.8,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-25DOI: 10.1016/j.media.2026.103961
Xin You, Ming Ding, Minghui Zhang, Hanxiao Zhang, Junyang Wu, Yi Yu, Jie Yang, Yun Gu
Accurate boundary segmentation of volumetric images is a critical task for image-guided diagnosis and computer-assisted intervention. It is challenging to address the boundary confusion with explicit constraints. Existing methods of refining boundaries overemphasize the slender structure while overlooking the dynamic interactions between boundaries and neighboring regions. In this paper, we reconceptualize the mechanism of boundary generation via introducing Pushing and Pulling interactions, then propose a unified network termed PP-Net to model shape characteristics of the confused boundary region. Specifically, we first propose the semantic difference module (SDM) from the pushing branch to drive the boundary towards the ground truth under diffusion guidance. Additionally, the class clustering module (CCM) from the pulling branch is introduced to stretch the intersected boundary along the opposite direction. Thus, pushing and pulling branches will furnish two adversarial forces to enhance representation capabilities for the faint boundary. Experiments are conducted on four public datasets and one in-house dataset plagued by boundary confusion. The results demonstrate the superiority of PP-Net over other segmentation networks, especially on the evaluation metrics of Hausdorff Distance and Average Symmetric Surface Distance. Besides, SDM and CCM can serve as plug-and-play modules to enhance classic U-shape baseline models, including recent SAM-based foundation models. Source codes are available at https://github.com/EndoluminalSurgicalVision-IMR/PnPNet.
{"title":"Towards Boundary Confusion for Volumetric Medical Image Segmentation","authors":"Xin You, Ming Ding, Minghui Zhang, Hanxiao Zhang, Junyang Wu, Yi Yu, Jie Yang, Yun Gu","doi":"10.1016/j.media.2026.103961","DOIUrl":"https://doi.org/10.1016/j.media.2026.103961","url":null,"abstract":"Accurate boundary segmentation of volumetric images is a critical task for image-guided diagnosis and computer-assisted intervention. It is challenging to address the boundary confusion with explicit constraints. Existing methods of refining boundaries overemphasize the slender structure while overlooking the dynamic interactions between boundaries and neighboring regions. In this paper, we reconceptualize the mechanism of boundary generation via introducing Pushing and Pulling interactions, then propose a unified network termed PP-Net to model shape characteristics of the confused boundary region. Specifically, we first propose the semantic difference module (SDM) from the pushing branch to drive the boundary towards the ground truth under diffusion guidance. Additionally, the class clustering module (CCM) from the pulling branch is introduced to stretch the intersected boundary along the opposite direction. Thus, pushing and pulling branches will furnish two adversarial forces to enhance representation capabilities for the faint boundary. Experiments are conducted on four public datasets and one in-house dataset plagued by boundary confusion. The results demonstrate the superiority of PP-Net over other segmentation networks, especially on the evaluation metrics of Hausdorff Distance and Average Symmetric Surface Distance. Besides, SDM and CCM can serve as plug-and-play modules to enhance classic U-shape baseline models, including recent SAM-based foundation models. Source codes are available at <ce:inter-ref xlink:href=\"https://github.com/EndoluminalSurgicalVision-IMR/PnPNet\" xlink:type=\"simple\">https://github.com/EndoluminalSurgicalVision-IMR/PnPNet</ce:inter-ref>.","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"291 1","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-24DOI: 10.1016/j.media.2026.103959
Donghan Wu , Wenyue Shen , Lu Yuan , Heng Li , Huaying Hao , Juan Ye , Yitian Zhao
Retinopathy of Prematurity (ROP) is a leading cause of childhood blindness worldwide. In clinical practice, fundus imaging serves as a primary diagnostic tool for ROP, making the accurate quality assessment of these images critically important. However, existing automated methods for evaluating ROP fundus images face significant challenges. First, there is a high degree of visual similarity between lesions and factors that influence quality. Second, there is a paucity of trustworthy outputs and interpretable or clinical-friendly designs, which limit their reliability and effectiveness. In this work, we propose a ROP image quality assessment framework, termed Q-ROP. This framework leverages fine-grained multi-label annotations based on key image factors such as artifacts, illumination, spatial positioning, and structural clarity. Additionally, the integration of a label graph network with evidential learning theory enables the model to explicitly capture the relationships between quality grades and influencing factors, thereby improving both robustness and accuracy. This approach facilitates interpretable analysis by directing the model’s focus toward relevant image features and reducing interference from lesion-like artifacts. Furthermore, the incorporation of evidential learning theory serves to quantify the uncertainty inherent in quality ratings, thereby ensuring the trustworthiness of the assessments. Trained and tested on a dataset of 6677 ROP images across three quality levels (i.e. acceptable, potentially acceptable, and unacceptable), Q-ROP achieved state-of-the-art performance with a 95.82% accuracy. Its effectiveness was further validated in a downstream ROP staging task, where it significantly improved the performance of typical classification models. These results demonstrate Q-ROP’s strong potential as a reliable and robust tool for clinical decision support.
{"title":"Fundus image quality assessment in retinopathy of prematurity via multi-label graph evidential network","authors":"Donghan Wu , Wenyue Shen , Lu Yuan , Heng Li , Huaying Hao , Juan Ye , Yitian Zhao","doi":"10.1016/j.media.2026.103959","DOIUrl":"10.1016/j.media.2026.103959","url":null,"abstract":"<div><div>Retinopathy of Prematurity (ROP) is a leading cause of childhood blindness worldwide. In clinical practice, fundus imaging serves as a primary diagnostic tool for ROP, making the accurate quality assessment of these images critically important. However, existing automated methods for evaluating ROP fundus images face significant challenges. First, there is a high degree of visual similarity between lesions and factors that influence quality. Second, there is a paucity of trustworthy outputs and interpretable or clinical-friendly designs, which limit their reliability and effectiveness. In this work, we propose a ROP image quality assessment framework, termed Q-ROP. This framework leverages fine-grained multi-label annotations based on key image factors such as artifacts, illumination, spatial positioning, and structural clarity. Additionally, the integration of a label graph network with evidential learning theory enables the model to explicitly capture the relationships between quality grades and influencing factors, thereby improving both robustness and accuracy. This approach facilitates interpretable analysis by directing the model’s focus toward relevant image features and reducing interference from lesion-like artifacts. Furthermore, the incorporation of evidential learning theory serves to quantify the uncertainty inherent in quality ratings, thereby ensuring the trustworthiness of the assessments. Trained and tested on a dataset of 6677 ROP images across three quality levels (i.e. acceptable, potentially acceptable, and unacceptable), Q-ROP achieved state-of-the-art performance with a 95.82% accuracy. Its effectiveness was further validated in a downstream ROP staging task, where it significantly improved the performance of typical classification models. These results demonstrate Q-ROP’s strong potential as a reliable and robust tool for clinical decision support.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103959"},"PeriodicalIF":11.8,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-24DOI: 10.1016/j.media.2026.103943
Nicholas Konz , Richard Osuala , Preeti Verma , Yuwen Chen , Hanxue Gu , Haoyu Dong , Yaqian Chen , Andrew Marshall , Lidia Garrucho , Kaisar Kushibar , Daniel M. Lang , Gene S. Kim , Lars J. Grimm , John M. Lewin , James S. Duncan , Julia A. Schnabel , Oliver Diaz , Karim Lekadir , Maciej A. Mazurowski
Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, to evaluate the output quality of image generative models. Currently, metrics used for this task either rely on the (potentially biased) choice of some downstream task, such as segmentation, or adopt task-independent perceptual metrics (e.g., Fréchet Inception Distance/FID) from natural imaging, which we show insufficiently capture anatomical features. To this end, we introduce a new perceptual metric tailored for medical images, FRD (Fréchet Radiomic Distance), which utilizes standardized, clinically meaningful, and interpretable image features. We show that FRD is superior to other image distribution metrics for a range of medical imaging applications, including out-of-domain (OOD) detection, the evaluation of image-to-image translation (by correlating more with downstream task performance as well as anatomical consistency and realism), and the evaluation of unconditional image generation. Moreover, FRD offers additional benefits such as stability and computational efficiency at low sample sizes, sensitivity to image corruptions and adversarial attacks, feature interpretability, and correlation with radiologist-perceived image quality. Additionally, we address key gaps in the literature by presenting an extensive framework for the multifaceted evaluation of image similarity metrics in medical imaging—including the first large-scale comparative study of generative models for medical image translation—and release an accessible codebase to facilitate future research. Our results are supported by thorough experiments spanning a variety of datasets, modalities, and downstream tasks, highlighting the broad potential of FRD for medical image analysis.
{"title":"Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets","authors":"Nicholas Konz , Richard Osuala , Preeti Verma , Yuwen Chen , Hanxue Gu , Haoyu Dong , Yaqian Chen , Andrew Marshall , Lidia Garrucho , Kaisar Kushibar , Daniel M. Lang , Gene S. Kim , Lars J. Grimm , John M. Lewin , James S. Duncan , Julia A. Schnabel , Oliver Diaz , Karim Lekadir , Maciej A. Mazurowski","doi":"10.1016/j.media.2026.103943","DOIUrl":"10.1016/j.media.2026.103943","url":null,"abstract":"<div><div>Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, to evaluate the output quality of image generative models. Currently, metrics used for this task either rely on the (potentially biased) choice of some downstream task, such as segmentation, or adopt task-independent perceptual metrics (<em>e.g.</em>, Fréchet Inception Distance/FID) from natural imaging, which we show insufficiently capture anatomical features. To this end, we introduce a new perceptual metric tailored for medical images, FRD (Fréchet Radiomic Distance), which utilizes standardized, clinically meaningful, and interpretable image features. We show that FRD is superior to other image distribution metrics for a range of medical imaging applications, including out-of-domain (OOD) detection, the evaluation of image-to-image translation (by correlating more with downstream task performance as well as anatomical consistency and realism), and the evaluation of unconditional image generation. Moreover, FRD offers additional benefits such as stability and computational efficiency at low sample sizes, sensitivity to image corruptions and adversarial attacks, feature interpretability, and correlation with radiologist-perceived image quality. Additionally, we address key gaps in the literature by presenting an extensive framework for the multifaceted evaluation of image similarity metrics in medical imaging—including the first large-scale comparative study of generative models for medical image translation—and release an accessible codebase to facilitate future research. Our results are supported by thorough experiments spanning a variety of datasets, modalities, and downstream tasks, highlighting the broad potential of FRD for medical image analysis.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103943"},"PeriodicalIF":11.8,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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