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1.
Alaya, M.B.* ; Lang, D.M. ; Wiestler, B.* ; Schnabel, J.A. & Bercea, C.-I.: MedEdit: Counterfactual diffusion-based image editing on brain MRI. In: (Simulation and Synthesis in Medical Imaging). 2025. 167-176 (Lect. Notes Comput. Sc. ; 15187 LNCS)
2.
Chobola, T. ; Liu, Y.* ; Zhang, H. ; Schnabel, J.A. & Peng, T.: Fast context-based low-light image enhancement via neural implicit representations. In: (Computer Vision – ECCV 2024). Gewerbestrasse 11, Cham, Ch-6330, Switzerland: Springer International Publishing Ag, 2025. 413-430 (Lect. Notes Comput. Sc. ; 15144)
3.
Daum, D. et al.: On differentially private 3D medical image synthesis with controllable latent diffusion models. In: (Deep Generative Models). 2025. 139-149 (Lect. Notes Comput. Sc. ; 15224 LNCS)
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Duelmer, F. et al.: PHOCUS: Physics-based deconvolution for ultrasound resolution enhancement. In: (Simplifying Medical Ultrasound). 2025. 35-44 (Lect. Notes Comput. Sc. ; 15186 LNCS)
5.
Durrer, A.* et al.: Denoising diffusion models for 3D healthy brain tissue inpainting. In: (Deep Generative Models). 2025. 87-97 (Lect. Notes Comput. Sc. ; 15224 LNCS)
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Farjallah, E.* et al.: Affordable deep learning for diagnosing inherited and common retinal diseases via color fundus photography. In: (Ophthalmic Medical Image Analysis). 2025. 83-93 (Lect. Notes Comput. Sc. ; 15188 LNCS)
7.
Kaess, P.* et al.: Fair and private CT contrast agent detection. In: (Ethics and Fairness in Medical Imaging). 2025. 34-45 (Lect. Notes Comput. Sc. ; 15198 LNCS)
8.
Kim, J.M. ; Bader, J. ; Alaniz, S. ; Schmid, C.* & Akata, Z.: DataDream: Few-shot guided dataset generation. In: (Computer Vision – ECCV 2024). 2025. 252-268 (Lect. Notes Comput. Sc. ; 15129 LNCS)
9.
Mykula, H.* et al.: Diffusion models for unsupervised anomaly detection in fetal brain ultrasound. In: (Simplifying Medical Ultrasound). 2025. 220-230 (Lect. Notes Comput. Sc. ; 15186 LNCS)
10.
Rasheed, H. et al.: Learning to match 2D keypoints across preoperative MR and intraoperative ultrasound. In: (Simplifying Medical Ultrasound). 2025. 78-87 (Lect. Notes Comput. Sc. ; 15186 LNCS)
11.
Singhi, N.* ; Kim, J.M.* ; Roth, K.* & Akata, Z.: Improving intervention efficacy via concept realignment in concept bottleneck models. In: (Computer Vision – ECCV 2024). 2025. 422-438 (Lect. Notes Comput. Sc. ; 15084 LNCS)
12.
Torren Peraire, P. et al.: Improving route development using convergent retrosynthesis planning. In: (1st International Workshop on AI in Drug Discovery (AIDD)). Gewerbestrasse 11, Cham, Ch-6330, Switzerland: Springer International Publishing Ag, 2025. 165-166 (Lect. Notes Comput. Sc. ; 14894)
13.
Bercea, C.-I. ; Wiestler, B.* ; Rueckert, D.* & Schnabel, J.A.: Diffusion models with implicit guidance for medical anomaly detection. In: (27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, 06-10 October 2024, Marrakesh, Morocco). Gewerbestrasse 11, Cham, Ch-6330, Switzerland: Springer International Publishing Ag, 2024. 211-220 (Lect. Notes Comput. Sc. ; 15011)
14.
Clevert, D.A.* ; Wand, M.* ; Malinovská, K.* ; Schmidhuber, J.* & Tetko, I.V.: Preface. Lect. Notes Comput. Sc. 14894 LNCS, v-vi (2024)
15.
De Benetti, F.* et al.: CloverNet – Leveraging planning annotations for enhanced procedural MR segmentation: An application to adaptive radiation therapy. In: (Clinical Image-Based Procedures). 2024. 1-10 (Lect. Notes Comput. Sc. ; 15196 LNCS)
16.
Deutges, M. ; Sadafi, A. ; Navab, N.* & Marr, C.: Neural cellular automata for lightweight, robust and explainable classification of white blood cell images. In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2024). Gewerbestrasse 11, Cham, Ch-6330, Switzerland: Springer International Publishing Ag, 2024. 693-702 (Lect. Notes Comput. Sc. ; 15003)
17.
di Folco, M. ; Bercea, C.-I. ; Chan, E. & Schnabel, J.A.: Interpretable representation learning of cardiac MRI via attribute regularization. In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2024). Gewerbestrasse 11, Cham, Ch-6330, Switzerland: Springer International Publishing Ag, 2024. 492-501 (Lect. Notes Comput. Sc. ; 15010)
18.
Eichhorn, H. et al.: Physics-informed deep learning for motion-corrected reconstruction of quantitative brain MRI. In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2024). Gewerbestrasse 11, Cham, Ch-6330, Switzerland: Springer International Publishing Ag, 2024. 562-571 (Lect. Notes Comput. Sc. ; 15007)
19.
Fischer, S.M. et al.: Progressive growing of patch size: Resource-efficient curriculum learning for dense prediction tasks. In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2024). 2024. 510-520 (Lect. Notes Comput. Sc. ; 15009 LNCS)
20.
Galter, I. ; Schneltzer, E. ; Marr, C. ; Spielmann, N. & Hrabě de Angelis, M.: EchoVisuAL: Efficient Segmentation of Echocardiograms Using Deep Active Learning. In: (Medical Image Understanding and Analysis). Gewerbestrasse 11, Cham, Ch-6330, Switzerland: Springer International Publishing Ag, 2024. 366-381 (Lect. Notes Comput. Sc. ; 14860 LNCS)
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