Pancreatic ductal adenocarcinoma (PDAC) segmentation on contrast-enhanced CT is inherently ambiguous: inter-rater disagreement among experts reflects genuine uncertainty rather than annotation noise. Standard deep learning approaches assume a single ground truth, producing probabilistic outputs that can be poorly calibrated and difficult to interpret under such ambiguity. We present TwinTrack, a framework that addresses this gap through post-hoc calibration of ensemble segmentation probabilities to the empirical mean human response (MHR), the fraction of expert annotators labeling a voxel as tumor. Calibrated probabilities are thus directly interpretable as the expected proportion of annotators assigning the tumor label, explicitly modeling inter-rater disagreement. The proposed post-hoc calibration procedure is simple and requires only a small multi-rater calibration set. It consistently improves calibration metrics over standard approaches when evaluated on the MICCAI 2025 CURVAS-PDACVI multi-rater benchmark.
@inproceedings{kirscher2026twintrack,title={TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation},author={Kirscher, Tristan and Ertl, Alexandra and Maier-Hein, Klaus and Coubez, Xavier and Meyer, Philippe and Faisan, Sylvain},booktitle={Medical Imaging with Deep Learning},year={2026},address={Taipei, Taiwan},}
Pediatric medical image segmentation remains challenging due to limited annotated data and significant anatomical variability across developmental stages. We propose PSAT, a framework that leverages adult data augmentations and transfer learning strategies to improve pediatric segmentation performance.
@inproceedings{kirscher2025psat,title={PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning},author={Kirscher, Tristan and Faisan, Sylvain and Coubez, Xavier and Barrier, Loris and Meyer, Philippe},booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},year={2025},address={Daejeon, South Korea},}
This work introduces a novel methodological framework for analyzing health trajectories and survival outcomes in heart failure patients. We combine NLP techniques for characterizing patient trajectories, unsupervised clustering with a new metric for measuring diagnosis distances, and survival analysis to assess patient outcomes.
@inproceedings{murris2024health,title={A Novel Methodological Framework for the Analysis of Health Trajectories and Survival Outcomes in Heart Failure Patients},author={Murris, Juliette and Amadei, Tristan and Kirscher, Tristan and Klein, Antoine and Tropeano, Anne-Isabelle and Katsahian, Sandrine},booktitle={ICLR 2024 Workshop on Learning from Time Series For Health},year={2024},address={Vienna, Austria},}
