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SASHIMI 2026: Simulation and Synthesis in Medical Imaging

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Submission Date:
2026-07-08 Extended
Notification Date:
2026-07-31
Conference Date:
2026-10-01
Location:
Strasbourg, France
ICORE: C   Viewed: 898   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

46.8 / 100
Ranked #2,352 of 5,684 conferences · Top 42%

#64 of 183 in Bioinformatics & Health Informatics #117 of 245 in Computer Vision & Pattern Recognition

Academic recognition (35%)
58
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%) No data - scored at the neutral baseline of 50 —
Community attention (10%)
12
Public record completeness (15%)
35

Inputs used: Listed as ICORE C · Researchers who opened this page in the past 24 months: 4

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 60% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-24

Call For Papers

SASHIMI 2026 (Simulation and Synthesis in Medical Imaging) is a ICORE C conference held in Strasbourg, France on 2026-10-01. The paper submission deadline is 2026-07-08 (extended). Acceptance notifications are sent on 2026-07-31.

Scope of the Workshop: The Medical Image Computing and Computer Assisted Intervention (MICCAI) community needs data with known ground truth to develop, evaluate, and validate computerized image analytic tools, as well as to facilitate clinical training. Synthetic data are ideally suited for this purpose. Another motivation to generate synthetic data is to improve the generalizability of deep learning and machine learning algorithms that are affected by domain shift issues. To generate synthetic data, a full range of models underpinning image simulation and synthesis, also referred to as image translation, cross-modality synthesis, image completion, domain adaptation, etc. have been developed over the years: (i) deep learning methods including fully-supervised, semi-supervised, self-supervised, unsupervised, transfer, and multi-task learning; (ii) deep learning model architectures including Generative Adversarial Network (GAN), Variational Auto-Encoder (VAE), Flows, Transformers, and etc; (iii) machine learning methods using hand-crafted features; (iv) detailed mechanistic models (top–down), which incorporate priors on the geometry and physics of image acquisition and formation processes; (v) complex spatio-temporal computational models of anatomical variability, organ physiology, and morphological changes in tissues or disease progression; (vi) applications of synthetic images including improving image quality, segmentation, tracking, detection, registration, and etc. The goal of the Simulation and Synthesis in Medical Imaging (SASHIMI) workshop is to bring together all those interested in such problems in order to engage in invigorating research, discuss current approaches, and stimulate new ideas and scientific directions in this field. The objectives are to (a) bring together experts on image synthesis to raise the state of the art; (b) hear from invited speakers outside of the MICCAI community, for example in the areas of transfer learning, generative adversarial networks, or variational autoencoders, to cross-fertilize these fields; and (c) identify challenges and opportunities for further research. We also want to identify the suitable approaches to evaluate the plausibility of synthetic data and to collect benchmark data that could help with the development of future algorithms. Topics: Topics of interest include, but are not limited to, the following: Fundamental methods for image-based biophysical modeling and image synthesis Biophysical and data-driven models of disease progression, organ development, motion and deformation, image formation and acquisition Virtual cell imaging Segmentation/registration across or within modalities to aid the learning of model parameters Imaging protocol harmonization approaches across imaging systems, sites and time points Image synthesis for normalization and spatio-temporal intensity correction Cross modality (PET/MR, PET/CT, CT/MR, etc.) image synthesis Simulation and synthesis from large-scale databases Machine and deep learning techniques in image simulation and synthesis Handling uncertainty and incomplete data via simulation and synthesis techniques Automated techniques for quality assessment of simulations and synthetic images Image synthesis in high dimensional spaces (vectors, tensors, spatio-temporal features, etc.) Handling uncertainty and incomplete data via simulation and synthesis techniques Evaluation and benchmarking of state of-the-art approaches in simulation and synthesis Normative and annotated datasets for benchmarking and learning models Novel ideas on evaluation metrics and methods in image-based simulation and image synthesis Applications of image synthesis in super resolution imaging and multi/cross-scale regression Applications of image synthesis and simulation in medical image registration and segmentation Applications of image synthesis/simulation in super resolution imaging and multi/cross-scale regression, registration, segmentation, denoising, fusion reconstruction and real-time simulation of biophysical properties Applications of synthesis and simulation to image reconstruction from sparse data or sparse views
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