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Benchmarking Foundation Models for Renal Lesion Stratification in CT
Authors:
Hartmut Häntze,
Sarah de Boer,
Myrthe Buser,
Alessa Hering,
Bram van Ginneken,
Mathias Prokop,
Jawed Nawabi,
Sebastian Ziegelmayer,
Lisa Adams,
Keno Bressem
Abstract:
The rapid proliferation of open-source medical foundation models (FMs) raises a practical question: how well do their pre-trained representations transfer to clinically relevant but data-scarce classification tasks? Particularly in CT-based renal lesion classification, a push toward greater generalizability would be meaningful, as the field is constrained by inherently limited training data. We ad…
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The rapid proliferation of open-source medical foundation models (FMs) raises a practical question: how well do their pre-trained representations transfer to clinically relevant but data-scarce classification tasks? Particularly in CT-based renal lesion classification, a push toward greater generalizability would be meaningful, as the field is constrained by inherently limited training data. We addressed this through a benchmark of three medical FMs on this specific task. This six-class problem spans common entities like cysts and clear cell renal cell carcinoma, alongside rare subtypes. Using a frozen feature-probing protocol, we compared FM embeddings against a handcrafted radiomics classifier and a 3D ResNet-50 trained from scratch. Models were trained on a composite dataset of 2,854 lesions and evaluated on an external test set of 234 lesions from The Cancer Imaging Archive. Our results reveal two key findings. First, FM performance (AUC 0.70-0.77) matched the from-scratch ResNet (AUC 0.72) while drastically reducing hardware demand, requiring only seconds on a CPU after feature extraction. However, the conventional radiomics baseline significantly outperformed all deep learning approaches, achieving an AUC of 0.88 (all p $\leq$ 0.002). This suggests that current generalist FM embeddings do not yet capture the fine-grained texture and shape heterogeneity driving histological subtype discrimination. Despite their potential in data-scarce settings, medical FMs did not surpass established models for renal lesion stratification, leaving radiomics as the current state-of-the-art.
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Submitted 8 May, 2026;
originally announced May 2026.
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End-user validation of BRIGHT with custom-developed graphical user interface applied to cervical cancer brachytherapy
Authors:
Leah R. M. Dickhoff,
Ellen M. Kerkhof,
Heloisa H. Deuzeman,
Laura A. Velema,
Stephanie M. de Boer,
Lavinia A. L. Verhagen,
Danique L. J. Barten,
Bradley R. Pieters,
Lukas J. A. Stalpers,
Renzo J. Scholman,
Pedro M. Matos,
Anton Bouter,
Carien L. Creutzberg,
Peter A. N. Bosman,
Tanja Alderliesten
Abstract:
Multi-objective optimisation using BRIGHT has proven insightful and effective in prostate cancer brachytherapy treatment planning. BRachytherapy via artificially Intelligent GOMEA-Heuristic based Treatment planning (BRIGHT) generates multiple treatment plans, each with a different trade-off between tumour coverage and organs-at-risk sparing. BRIGHT was recently extended to cervical cancer brachyth…
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Multi-objective optimisation using BRIGHT has proven insightful and effective in prostate cancer brachytherapy treatment planning. BRachytherapy via artificially Intelligent GOMEA-Heuristic based Treatment planning (BRIGHT) generates multiple treatment plans, each with a different trade-off between tumour coverage and organs-at-risk sparing. BRIGHT was recently extended to cervical cancer brachytherapy. In this study, we present a novel, custom-developed graphical user interface (GUI) that enables plan navigation, pairwise comparisons, dose distribution visualisation, and possibility for adjustments - essential for efficient clinical use of BRIGHT. End-user validation of BRIGHT with the dedicated GUI was conducted for cervical cancer brachytherapy by emulating clinical practice in ten previously treated patients. A multidisciplinary brachytherapy team used BRIGHT to create new treatment plans. GUI usability was assessed using the System Usability Scale (SUS). BRIGHT plan quality was compared to clinical practice via blinded one-on-one comparisons. The GUI offered helpful features for plan navigation and evaluation, giving users quick insight into whether planning aims are achievable and what treatment options are available. The overall SUS score was 83.3, indicating an 'excellent' system. BRIGHT outperformed clinical practice in five out of ten patients regarding the coverage-sparing trade-off and performed equally well in the remaining five. The BRIGHT plan was preferred over the clinical plan in eight out of ten patients, four of which showed clinically relevant differences. The clinical plan was preferred in two patients, neither with clinically relevant differences. In conclusion, BRIGHT, with its dedicated GUI, is a clinically viable and user-friendly tool for treatment planning in cervical cancer brachytherapy.
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Submitted 18 February, 2026;
originally announced February 2026.
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Kidney Cancer Detection Using 3D-Based Latent Diffusion Models
Authors:
Jen Dusseljee,
Sarah de Boer,
Alessa Hering
Abstract:
In this work, we present a novel latent diffusion-based pipeline for 3D kidney anomaly detection on contrast-enhanced abdominal CT. The method combines Denoising Diffusion Probabilistic Models (DDPMs), Denoising Diffusion Implicit Models (DDIMs), and Vector-Quantized Generative Adversarial Networks (VQ-GANs). Unlike prior slice-wise approaches, our method operates directly on an image volume and l…
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In this work, we present a novel latent diffusion-based pipeline for 3D kidney anomaly detection on contrast-enhanced abdominal CT. The method combines Denoising Diffusion Probabilistic Models (DDPMs), Denoising Diffusion Implicit Models (DDIMs), and Vector-Quantized Generative Adversarial Networks (VQ-GANs). Unlike prior slice-wise approaches, our method operates directly on an image volume and leverages weak supervision with only case-level pseudo-labels. We benchmark our approach against state-of-the-art supervised segmentation and detection models. This study demonstrates the feasibility and promise of 3D latent diffusion for weakly supervised anomaly detection. While the current results do not yet match supervised baselines, they reveal key directions for improving reconstruction fidelity and lesion localization. Our findings provide an important step toward annotation-efficient, generative modeling of complex abdominal anatomy.
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Submitted 9 January, 2026;
originally announced January 2026.
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EvalBlocks: A Modular Pipeline for Rapidly Evaluating Foundation Models in Medical Imaging
Authors:
Jan Tagscherer,
Sarah de Boer,
Lena Philipp,
Fennie van der Graaf,
Dré Peeters,
Joeran Bosma,
Lars Leijten,
Bogdan Obreja,
Ewoud Smit,
Alessa Hering
Abstract:
Developing foundation models in medical imaging requires continuous monitoring of downstream performance. Researchers are burdened with tracking numerous experiments, design choices, and their effects on performance, often relying on ad-hoc, manual workflows that are inherently slow and error-prone. We introduce EvalBlocks, a modular, plug-and-play framework for efficient evaluation of foundation…
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Developing foundation models in medical imaging requires continuous monitoring of downstream performance. Researchers are burdened with tracking numerous experiments, design choices, and their effects on performance, often relying on ad-hoc, manual workflows that are inherently slow and error-prone. We introduce EvalBlocks, a modular, plug-and-play framework for efficient evaluation of foundation models during development. Built on Snakemake, EvalBlocks supports seamless integration of new datasets, foundation models, aggregation methods, and evaluation strategies. All experiments and results are tracked centrally and are reproducible with a single command, while efficient caching and parallel execution enable scalable use on shared compute infrastructure. Demonstrated on five state-of-the-art foundation models and three medical imaging classification tasks, EvalBlocks streamlines model evaluation, enabling researchers to iterate faster and focus on model innovation rather than evaluation logistics. The framework is released as open source software at https://github.com/DIAGNijmegen/eval-blocks.
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Submitted 1 April, 2026; v1 submitted 7 January, 2026;
originally announced January 2026.
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Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework
Authors:
Sarah de Boer,
Hartmut Häntze,
Kiran Vaidhya Venkadesh,
Myrthe A. D. Buser,
Gabriel E. Humpire Mamani,
Lina Xu,
Lisa C. Adams,
Jawed Nawabi,
Keno K. Bressem,
Bram van Ginneken,
Mathias Prokop,
Alessa Hering
Abstract:
Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments. Kidney volume could serve as an important biomarker for renal diseases, with changes in volume correlating directly with kidney function. Currently, clinical practice often relies on subjective visual assessment for evaluating kidney size and kidney lesions,…
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Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments. Kidney volume could serve as an important biomarker for renal diseases, with changes in volume correlating directly with kidney function. Currently, clinical practice often relies on subjective visual assessment for evaluating kidney size and kidney lesions, including tumors and cysts, which are typically staged based on diameter, volume, and anatomical location. To support a more objective and reproducible approach, this research aims to develop a robust, thoroughly validated renal mass segmentation algorithm, named Renal-Net. We employ publicly available training datasets and leverage the state-of-the-art medical image segmentation framework nnU-Net. Validation is conducted using both proprietary and public test datasets, with segmentation performance quantified by Dice coefficient and the 95th percentile Hausdorff distance. Furthermore, we analyze robustness across subgroups based on patient sex, age, CT contrast phases, and tumor histologic subtypes. Our findings demonstrate that our segmentation algorithm, trained exclusively on publicly available data, generalizes effectively to external test sets and outperforms existing state-of-the-art models across all tested datasets. Subgroup analyses reveal consistent high performance, indicating strong robustness and reliability. The developed algorithm and associated code are publicly accessible at https://github.com/DIAGNijmegen/oncology-kidney-abnormality-segmentation.
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Submitted 8 June, 2026; v1 submitted 12 May, 2025;
originally announced May 2025.
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In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review
Authors:
Amelia Jiménez-Sánchez,
Natalia-Rozalia Avlona,
Sarah de Boer,
Víctor M. Campello,
Aasa Feragen,
Enzo Ferrante,
Melanie Ganz,
Judy Wawira Gichoya,
Camila González,
Steff Groefsema,
Alessa Hering,
Adam Hulman,
Leo Joskowicz,
Dovile Juodelyte,
Melih Kandemir,
Thijs Kooi,
Jorge del Pozo Lérida,
Livie Yumeng Li,
Andre Pacheco,
Tim Rädsch,
Mauricio Reyes,
Théo Sourget,
Bram van Ginneken,
David Wen,
Nina Weng
, et al. (4 additional authors not shown)
Abstract:
Datasets play a critical role in medical imaging research, yet issues such as label quality, shortcuts, and metadata are often overlooked. This lack of attention may harm the generalizability of algorithms and, consequently, negatively impact patient outcomes. While existing medical imaging literature reviews mostly focus on machine learning (ML) methods, with only a few focusing on datasets for s…
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Datasets play a critical role in medical imaging research, yet issues such as label quality, shortcuts, and metadata are often overlooked. This lack of attention may harm the generalizability of algorithms and, consequently, negatively impact patient outcomes. While existing medical imaging literature reviews mostly focus on machine learning (ML) methods, with only a few focusing on datasets for specific applications, these reviews remain static -- they are published once and not updated thereafter. This fails to account for emerging evidence, such as biases, shortcuts, and additional annotations that other researchers may contribute after the dataset is published. We refer to these newly discovered findings of datasets as research artifacts. To address this gap, we propose a living review that continuously tracks public datasets and their associated research artifacts across multiple medical imaging applications. Our approach includes a framework for the living review to monitor data documentation artifacts, and an SQL database to visualize the citation relationships between research artifact and dataset. Lastly, we discuss key considerations for creating medical imaging datasets, review best practices for data annotation, discuss the significance of shortcuts and demographic diversity, and emphasize the importance of managing datasets throughout their entire lifecycle. Our demo is publicly available at http://inthepicture.itu.dk/.
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Submitted 2 June, 2025; v1 submitted 18 January, 2025;
originally announced January 2025.
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Deformable MRI Sequence Registration for AI-based Prostate Cancer Diagnosis
Authors:
Alessa Hering,
Sarah de Boer,
Anindo Saha,
Jasper J. Twilt,
Mattias P. Heinrich,
Derya Yakar,
Maarten de Rooij,
Henkjan Huisman,
Joeran S. Bosma
Abstract:
The PI-CAI (Prostate Imaging: Cancer AI) challenge led to expert-level diagnostic algorithms for clinically significant prostate cancer detection. The algorithms receive biparametric MRI scans as input, which consist of T2-weighted and diffusion-weighted scans. These scans can be misaligned due to multiple factors in the scanning process. Image registration can alleviate this issue by predicting t…
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The PI-CAI (Prostate Imaging: Cancer AI) challenge led to expert-level diagnostic algorithms for clinically significant prostate cancer detection. The algorithms receive biparametric MRI scans as input, which consist of T2-weighted and diffusion-weighted scans. These scans can be misaligned due to multiple factors in the scanning process. Image registration can alleviate this issue by predicting the deformation between the sequences. We investigate the effect of image registration on the diagnostic performance of AI-based prostate cancer diagnosis. First, the image registration algorithm, developed in MeVisLab, is analyzed using a dataset with paired lesion annotations. Second, the effect on diagnosis is evaluated by comparing case-level cancer diagnosis performance between using the original dataset, rigidly aligned diffusion-weighted scans, or deformably aligned diffusion-weighted scans. Rigid registration showed no improvement. Deformable registration demonstrated a substantial improvement in lesion overlap (+10% median Dice score) and a positive yet non-significant improvement in diagnostic performance (+0.3% AUROC, p=0.18). Our investigation shows that a substantial improvement in lesion alignment does not directly lead to a significant improvement in diagnostic performance. Qualitative analysis indicated that jointly developing image registration methods and diagnostic AI algorithms could enhance diagnostic accuracy and patient outcomes.
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Submitted 28 June, 2024; v1 submitted 15 April, 2024;
originally announced April 2024.
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Dynamic Separation Logic
Authors:
Frank S. de Boer,
Hans-Dieter A. Hiep,
Stijn de Gouw
Abstract:
This paper introduces a dynamic logic extension of separation logic. The assertion language of separation logic is extended with modalities for the five types of the basic instructions of separation logic: simple assignment, look-up, mutation, allocation, and de-allocation. The main novelty of the resulting dynamic logic is that it allows to combine different approaches to resolving these modaliti…
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This paper introduces a dynamic logic extension of separation logic. The assertion language of separation logic is extended with modalities for the five types of the basic instructions of separation logic: simple assignment, look-up, mutation, allocation, and de-allocation. The main novelty of the resulting dynamic logic is that it allows to combine different approaches to resolving these modalities. One such approach is based on the standard weakest precondition calculus of separation logic. The other approach introduced in this paper provides a novel alternative formalization in the proposed dynamic logic extension of separation logic. The soundness and completeness of this axiomatization has been formalized in the Coq theorem prover.
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Submitted 24 January, 2025; v1 submitted 16 September, 2023;
originally announced September 2023.
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Proving Correctness of Parallel Implementations of Transition System Specifications
Authors:
Frank S. de Boer,
Einar Broch Johnsen,
Violet Ka I Pun,
Silvia Lizeth Tapia Tarifa
Abstract:
The overall problem addressed in this paper is the long-standing problem of program correctness, and in particular programs that describe systems of parallel executing processes. We propose a new method for proving correctness of parallel implementations of high-level transition system specifications. The implementation language underlying the method is based on the model of active (or concurrent)…
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The overall problem addressed in this paper is the long-standing problem of program correctness, and in particular programs that describe systems of parallel executing processes. We propose a new method for proving correctness of parallel implementations of high-level transition system specifications. The implementation language underlying the method is based on the model of active (or concurrent) objects. The method defines correctness in terms of a simulation relation between the transition system which specifies the program semantics and the transition system that is described by the correctness specification. The simulation relation itself abstracts from the fine-grained interleaving of parallel processes by exploiting a global confluence property of the particular model of active objects considered in this paper. As a proof-of-concept we apply our method to the correctness of a parallel simulator of multicore memory systems.
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Submitted 25 January, 2023;
originally announced February 2023.
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The Grammar of Emergent Languages
Authors:
Oskar van der Wal,
Silvan de Boer,
Elia Bruni,
Dieuwke Hupkes
Abstract:
In this paper, we consider the syntactic properties of languages emerged in referential games, using unsupervised grammar induction (UGI) techniques originally designed to analyse natural language. We show that the considered UGI techniques are appropriate to analyse emergent languages and we then study if the languages that emerge in a typical referential game setup exhibit syntactic structure, a…
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In this paper, we consider the syntactic properties of languages emerged in referential games, using unsupervised grammar induction (UGI) techniques originally designed to analyse natural language. We show that the considered UGI techniques are appropriate to analyse emergent languages and we then study if the languages that emerge in a typical referential game setup exhibit syntactic structure, and to what extent this depends on the maximum message length and number of symbols that the agents are allowed to use. Our experiments demonstrate that a certain message length and vocabulary size are required for structure to emerge, but they also illustrate that more sophisticated game scenarios are required to obtain syntactic properties more akin to those observed in human language. We argue that UGI techniques should be part of the standard toolkit for analysing emergent languages and release a comprehensive library to facilitate such analysis for future researchers.
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Submitted 9 October, 2020; v1 submitted 5 October, 2020;
originally announced October 2020.
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Verifying OpenJDK's LinkedList using KeY
Authors:
Hans-Dieter A. Hiep,
Olaf Maathuis,
Jinting Bian,
Frank S. de Boer,
Marko van Eekelen,
Stijn de Gouw
Abstract:
As a particular case study of the formal verification of state-of-the-art, real software, we discuss the specification and verification of a corrected version of the implementation of a linked list as provided by the Java Collection framework. Keywords: Java, standard library, deductive verification, KeY, Java Modeling Language, case study, bug
As a particular case study of the formal verification of state-of-the-art, real software, we discuss the specification and verification of a corrected version of the implementation of a linked list as provided by the Java Collection framework. Keywords: Java, standard library, deductive verification, KeY, Java Modeling Language, case study, bug
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Submitted 11 November, 2019;
originally announced November 2019.
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Reasoning about call-by-value: a missing result in the history of Hoare's logic
Authors:
Krzysztof R. Apt,
Frank S. de Boer
Abstract:
We provide a sound and relatively complete Hoare-like proof system for reasoning about partial correctness of recursive procedures in presence of local variables and the call-by-value parameter mechanism, and in which the correctness proofs are linear in the length of the program. We argue that in spite of the fact that Hoare-like proof systems for recursive procedures were intensively studied, no…
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We provide a sound and relatively complete Hoare-like proof system for reasoning about partial correctness of recursive procedures in presence of local variables and the call-by-value parameter mechanism, and in which the correctness proofs are linear in the length of the program. We argue that in spite of the fact that Hoare-like proof systems for recursive procedures were intensively studied, no such proof system has been proposed in the literature.
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Submitted 13 September, 2019;
originally announced September 2019.
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Analysis of SLA Compliance in the Cloud -- An Automated, Model-based Approach
Authors:
Frank S. de Boer,
Elena Giachino,
Stijn de Gouw,
Reiner Hähnle,
Einar Broch Johnsen,
Cosimo Laneve,
Ka I Pun,
Gianluigi Zavattaro
Abstract:
Service Level Agreements (SLA) are commonly used to specify the quality attributes between cloud service providers and the customers. A violation of SLAs can result in high penalties. To allow the analysis of SLA compliance before the services are deployed, we describe in this paper an approach for SLA-aware deployment of services on the cloud, and illustrate its workflow by means of a case study.…
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Service Level Agreements (SLA) are commonly used to specify the quality attributes between cloud service providers and the customers. A violation of SLAs can result in high penalties. To allow the analysis of SLA compliance before the services are deployed, we describe in this paper an approach for SLA-aware deployment of services on the cloud, and illustrate its workflow by means of a case study. The approach is based on formal models combined with static analysis tools and generated runtime monitors. As such, it fits well within a methodology combining software development with information technology operations (DevOps).
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Submitted 27 August, 2019;
originally announced August 2019.
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Multi-Threaded Actors
Authors:
Keyvan Azadbakht,
Frank S. de Boer,
Vlad Serbanescu
Abstract:
In this paper we introduce a new programming model of multi-threaded actors which feature the parallel processing of their messages. In this model an actor consists of a group of active objects which share a message queue. We provide a formal operational semantics, and a description of a Java-based implementation for the basic programming abstractions describing multi-threaded actors. Finally, we…
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In this paper we introduce a new programming model of multi-threaded actors which feature the parallel processing of their messages. In this model an actor consists of a group of active objects which share a message queue. We provide a formal operational semantics, and a description of a Java-based implementation for the basic programming abstractions describing multi-threaded actors. Finally, we evaluate our proposal by means of an example application.
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Submitted 10 August, 2016;
originally announced August 2016.
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Verification of Object-Oriented Programs: a Transformational Approach
Authors:
Krzysztof R. Apt,
Frank S. de Boer,
Ernst-Ruediger Olderog,
Stijn de Gouw
Abstract:
We show that verification of object-oriented programs by means of the assertional method can be achieved in a simple way by exploiting a syntax-directed transformation from object-oriented programs to recursive programs. This transformation suggests natural proofs rules and its correctness helps us to establish soundness and relative completeness of the proposed proof system. One of the difficulti…
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We show that verification of object-oriented programs by means of the assertional method can be achieved in a simple way by exploiting a syntax-directed transformation from object-oriented programs to recursive programs. This transformation suggests natural proofs rules and its correctness helps us to establish soundness and relative completeness of the proposed proof system. One of the difficulties is how to properly deal in the assertion language with the instance variables and aliasing. The discussed programming language supports arrays, instance variables, failures and recursive methods with parameters.
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Submitted 8 November, 2011; v1 submitted 26 April, 2010;
originally announced April 2010.
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Modular Verification of Recursive Programs
Authors:
Krzysztof R. Apt,
Frank S. de Boer,
Ernst-Rüdiger Olderog
Abstract:
We argue that verification of recursive programs by means of the assertional method of C.A.R. Hoare can be conceptually simplified using a modular reasoning. In this approach some properties of the program are established first and subsequently used to establish other program properties. We illustrate this approach by providing a modular correctness proof of the Quicksort program.
We argue that verification of recursive programs by means of the assertional method of C.A.R. Hoare can be conceptually simplified using a modular reasoning. In this approach some properties of the program are established first and subsequently used to establish other program properties. We illustrate this approach by providing a modular correctness proof of the Quicksort program.
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Submitted 24 July, 2009;
originally announced July 2009.
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Proving correctness of Timed Concurrent Constraint Programs
Authors:
F. S. de Boer,
M. Gabbrielli,
M. C. Meo
Abstract:
A temporal logic is presented for reasoning about the correctness of timed concurrent constraint programs. The logic is based on modalities which allow one to specify what a process produces as a reaction to what its environment inputs. These modalities provide an assumption/commitment style of specification which allows a sound and complete compositional axiomatization of the reactive behavior…
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A temporal logic is presented for reasoning about the correctness of timed concurrent constraint programs. The logic is based on modalities which allow one to specify what a process produces as a reaction to what its environment inputs. These modalities provide an assumption/commitment style of specification which allows a sound and complete compositional axiomatization of the reactive behavior of timed concurrent constraint programs.
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Submitted 28 August, 2002;
originally announced August 2002.
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Agent Programming with Declarative Goals
Authors:
F. S. de Boer,
K. V. Hindriks,
W. van der Hoek,
J. -J. Ch. Meyer
Abstract:
A long and lasting problem in agent research has been to close the gap between agent logics and agent programming frameworks. The main reason for this problem of establishing a link between agent logics and agent programming frameworks is identified and explained by the fact that agent programming frameworks have not incorporated the concept of a `declarative goal'. Instead, such frameworks have…
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A long and lasting problem in agent research has been to close the gap between agent logics and agent programming frameworks. The main reason for this problem of establishing a link between agent logics and agent programming frameworks is identified and explained by the fact that agent programming frameworks have not incorporated the concept of a `declarative goal'. Instead, such frameworks have focused mainly on plans or `goals-to-do' instead of the end goals to be realised which are also called `goals-to-be'. In this paper, a new programming language called GOAL is introduced which incorporates such declarative goals. The notion of a `commitment strategy' - one of the main theoretical insights due to agent logics, which explains the relation between beliefs and goals - is used to construct a computational semantics for GOAL. Finally, a proof theory for proving properties of GOAL agents is introduced. Thus, we offer a complete theory of agent programming in the sense that our theory provides both for a programming framework and a programming logic for such agents. An example program is proven correct by using this programming logic.
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Submitted 3 July, 2002;
originally announced July 2002.