Awards
Francois Erbsmann prize for best oral
220 - Bayesian Learning with Stochastic Perturbations and Langevin Expectation Maximization for Unsupervised DNN Image Quality Enhancement Vatsala Sharma, Suyash P. Awate In: Oguz, I., Zhang, S., Metaxas, D.N. (eds) Information Processing in Medical Imaging. IPMI 2025. Lecture Notes in Computer Science, vol 15829. Springer, Cham. Unsupervised learning of deep-neural-networks (DNNs) for image quality enhancement can overcome the real-world challenge of the lack of high-quality training images. Typical DNNs for weakly/un-supervised image restoration make strong assumptions that are often infeasible or undesirable in clinical scenarios, e.g., they (a) demand multiple acquired degraded instances per scene, (b) simulate degraded instances assuming independent identically-distributed noise per pixel, (c) demand pre-training large diffusion models on large sets of high(er)-quality images, or (d) ignore uncertainty estimation in their outputs. We propose a novel BayesianDNN framework for unsupervised image quality enhancement incorporating (i) stochastic perturbations at multiple stages within the DNN architecture, for regularization and data-driven automatic generation of realistic degraded instances, (ii) variational/distribution modeling in latent space, (iii) novel Monte-Carlo expectation maximization of DNN parameters using Langevin diffusion in latent space, and (iv) novel low-density sampling for perturbations using normalized Langevin diffusion. Results on publicly available datasets demonstrates the benefits of our DNN framework over existing methods in CT, MRI, and PET. Hide abstract
Runner-up best oral award
57 - Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography Yuexi Du, John Onofrey, Nicha C. DvornekIn: Oguz, I., Zhang, S., Metaxas, D.N. (eds) Information Processing in Medical Imaging. IPMI 2025. Lecture Notes in Computer Science, vol 15830. Springer, Cham. Contrastive Language-Image Pre-training (CLIP) demonstrates strong potential in medical image analysis but requires substantial data and computational resources. Due to these restrictions, existing CLIP applications in medical imaging focus mainly on modalities like chest X-rays that have abundant image-report data available, leaving many other important modalities under-explored. Here, we propose one of the first adaptations of the full CLIP model to mammography, which presents significant challenges due to labeled data scarcity, high-resolution images with small regions of interest, and class-wise imbalance. We first develop a specialized supervision framework for mammography that leverages its multi-view nature. Furthermore, we design a symmetric local alignment module to better focus on detailed features in high-resolution images. Lastly, we incorporate a parameter-efficient fine-tuning approach for large language models pre-trained with medical knowledge to address data limitations. Our multi-view and multi-scale alignment (MaMA) method outperforms state-of-the-art baselines for three different tasks on two large real-world mammography datasets, EMBED and RSNA-Mammo, with only 52% model size compared with the largest baseline. The code is available at https://github.com/XYPB/MaMA. Hide abstract
Best poster award
92 - Cycle-consistent zero-shot through-plane super-resolution for anisotropic head MRI Samuel Remedios, Shuwen Wei, Aaron Carass, Blake Dewey, Jerry PrinceIn: Oguz, I., Zhang, S., Metaxas, D.N. (eds) Information Processing in Medical Imaging. IPMI 2025. Lecture Notes in Computer Science, vol 15829. Springer, Cham. Magnetic resonance (MR) images are often acquired as anisotropic volumes in clinical settings. Such volumes have a worse through-plane resolution than in-plane resolution, hampering results in many processing pipelines that expect isotropic resolutions. Super-resolution (SR) is a promising methodology to address this problem, but there is concern whether the estimated high-resolution (HR) image suffers from egregious hallucinations, especially with deep learning methods that produce aesthetically pleasing results. One approach to restrict the impact of hallucinations is to guarantee that the estimated HR image is exactly cycle-consistent with the low-resolution observation. The denoising diffusion null space model (DDNM) achieves this through a range null space decomposition, but the specific design of the forward map is left to the application. In this work, we analyze the forward problem in 2D MR acquisition and construct an appropriate linear map A. We train a denoising diffusion probabilistic model on T1-weighted (T1-w) head MR images from multiple datasets and implement DDNM using A for the SR task. We show that the approach yields exact cycle-consistent solutions that are also realistic. We evaluated the approach in a wide variety of T1-w MR datasets, including withheld subjects from training sites and two sites outside of the training domain. We achieve excellent qualitative and quantitative results according to both distortion and perceptual metrics. Hide abstract
