8 papers
Assessing nnU-Net Generalization across Brain Tumor Populations in BraTS-GoAT 2026
Tristan Kirscher, Vivian Metzger, Philippe Meyer +1
BraTS-GoAT evaluates tumor segmentation across heterogeneous populations. We trained a conventional 3D nnU-Net on 1,351 labeled cases using five-fold cross-validation and 1,000 epo…
Rethinking Post-Hoc Calibration in Semantic Segmentation
Tristan Kirscher, Kim-Celine Kahl, Balint Kovacs +5
Reliable confidence estimates are essential in semantic segmentation, yet modern models often remain miscalibrated. We investigate two overlooked issues in post-hoc calibration. Fi…
TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation
Tristan Kirscher, Alexandra Ertl, Klaus Maier-Hein +3
Pancreatic ductal adenocarcinoma (PDAC) segmentation on contrast-enhanced CT is inherently ambiguous: inter-rater disagreement among experts reflects genuine uncertainty rather tha…
Assessing Pancreatic Ductal Adenocarcinoma Vascular Invasion: the PDACVI Benchmark
M. Riera-Marín, O. K. Sikha, J. Rodríguez-Comas +23
Surgical resection remains the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC), and eligibility depends on accurate assessment of vascular invasion…
PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning
Tristan Kirscher, Sylvain Faisan, Xavier Coubez +2
Pediatric medical imaging presents unique challenges due to significant anatomical and developmental differences compared to adults. Direct application of segmentation models train…
ImmunoFOMO: Are Language Models missing what oncologists see?
Aman Sinha, Bogdan-Valentin Popescu, Xavier Coubez +2
Language models (LMs) capabilities have grown with a fast pace over the past decade leading researchers in various disciplines, such as biomedical research, to increasingly explore…
Domain-specific or Uncertainty-aware models: Does it really make a difference for biomedical text classification?
Aman Sinha, Timothee Mickus, Marianne Clausel +2
The success of pretrained language models (PLMs) across a spate of use-cases has led to significant investment from the NLP community towards building domain-specific foundational…
No Imputation Needed: A Switch Approach to Irregularly Sampled Time Series
Rohit Agarwal, Aman Sinha, Ayan Vishwakarma +5
Modeling irregularly-sampled time series (ISTS) is challenging because of missing values. Most existing methods focus on handling ISTS by converting irregularly sampled data into r…