Cancers
Convolutional, State-Space and Transformer Liver-Tumor Segmentation Models Trained on Hepatocellular Carcinoma Fail on the Same External Cases: Lesion Size Outweighs Architecture in Cross-Dataset Generalization
By Kutlu H, Çolak C and Akbulut S.
Abstract
Background/objectives
Architectural innovation is widely assumed to improve the cross-institutional generalization of tumor segmentation models, yet this is rarely tested. We compared three architectural families under an identical, leak-safe protocol in hepatocellular carcinoma (HCC) segmentation.
Methods
Convolutional (nnU-Net ResEnc-M), state-space (U-Mamba Bot) and transformer (SwinUNETR) models were trained on HCC-TACE-Seg (n = 103) with identical patient-disjoint five-fold splits, then applied without adaptation to 3D-IRCADb (n = 15) and LiTS (n = 118). We assessed discrimination, case-level failure concordance, lesion-volume dependence, calibration and selective prediction.