Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets

Soumen Ghosh; Christine Jestin Hannan; Rajat Vashistha; Parveen Kundu; Sandra Brosda; Lauren G. Aoude; James Lonie; Andrew Nathanson; Jessica Ng; Andrew P. Barbour; Viktor Vegh

arXiv · 2508.18612

Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets

Soumen Ghosh·Christine Jestin Hannan·Rajat Vashistha·Parveen Kundu·Sandra Brosda·Lauren G. Aoude·James Lonie·Andrew Nathanson·Jessica Ng·Andrew P. Barbour·Viktor Vegh

View original source ↗Read source document ↗Export metadata ↓

Abstract

Robust generalization is essential for deploying deep learning based tumor segmentation in clinical PET-CT workflows, where anatomical sites, scanners, and patient populations vary widely. This study presents the first cross cancer evaluation of nnU-Net on PET-CT, introducing two novel, expert-annotated whole-body datasets. 279 patients with oesophageal cancer (Australian cohort) and 54 with lung cancer (Indian cohort). These cohorts complement the public AutoPET dataset and enable systematic stress-testing of cross domain performance. We trained and tested 3D nnUNet models under three paradigms. Target only (oesophageal), public only (AutoPET), and combined training. For the tested sets, the oesophageal only model achieved the best in-domain accuracy (mean DSC, 57.8) but failed on external Indian lung cohort (mean DSC less than 3.4), indicating severe overfitting. The public only model generalized more broadly (mean DSC, 63.5 on AutoPET, 51.6 on Indian lung cohort) but underperformed in oesophageal Australian cohort (mean DSC, 26.7). The combined approach provided the most balanced results (mean DSC, lung (52.9), oesophageal (40.7), AutoPET (60.9)), reducing boundary errors and improving robustness across all cohorts. These findings demonstrate that dataset diversity, particularly multi demographic, multi center and multi cancer integration, outweighs architectural novelty as the key driver of robust generalization. This work presents the demography based cross cancer deep learning segmentation evaluation and highlights dataset diversity, rather than model complexity, as the foundation for clinically robust segmentation.

Explore related subjects

eess.IV cs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Soumen Ghosh, Christine Jestin Hannan, Rajat Vashistha, Parveen Kundu, Sandra Brosda, Lauren G. Aoude, James Lonie, Andrew Nathanson, Jessica Ng, Andrew P. Barbour, Viktor Vegh. 2025-08-26. Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets. https://arxiv.org/abs/2508.18612

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

KELP: K-space-conditioned Estimation of Learned Sampling Patterns for Scan-Adaptive Multi-Coil MRI

Deep learning techniques have gained considerable attention for accelerating MRI acquisition while maintaining image quality. In this work, we present a convolutional neural network (CNN)-based framework for predicting scan-adaptive undersampling patterns directly from low-frequency multi-coil $k$-space data. Unlike approaches that optimize sampling patterns during training or rely on nearest-neighbor search at inference, our method is trained using precomputed scan-adaptive optimized masks as supervised labels and predicts a scan-specific sampling pattern in a single forward pass. The training procedure alternates between optimizing the sampling network and a reconstruction network, allowing the learned masks to adapt to reconstruction performance. Experiments on the fastMRI multi-coil knee dataset demonstrate competitive reconstruction quality compared with existing population- and scan-adaptive sampling approaches at $4\times$ and $8\times$ acceleration factors. In addition, the proposed method enables efficient scan-specific mask prediction with inference cost independent of the training dictionary size.

eess.IV↗

arXiv2026

LaminoDiff: Generative Computed Laminography via Near-Isotropic Spectral Supervision and Anisotropic Geometry

Computed Laminography (CL) is widely used for nondestructive inspection of extended planar objects, but its restricted angular coverage produces an anisotropic point spread function, missing-cone spectral incompleteness, and severe aliasing with interlayer leakage. This paper presents LaminoDiff, a physics-constrained diffusion framework for CL reconstruction. During training, a CT-derived near-isotropic supervision target is reconstructed from full-angle Computed Tomography (CT) projections physically degraded to match CL detector noise and focal-spot blur while retaining full angular coverage; it is withheld from inference. At inference, the reverse process uses only the CL observation. An Anisotropic Representation (AR) constructs depth-aware channels from three adjacent slices: a neighbor average, a center-neighbor residual, and the retained current slice, followed by directional in-plane feature extraction. Experiments on simulated and real multilayer printed circuit board data, including ball grid array and high-frequency stub samples, show that LaminoDiff improves artifact suppression, edge preservation, and depth stratification over analytic Feldkamp--Davis--Kress and representative learning-based baselines.

eess.IV↗

arXiv2026

IViT: A Novel Interpretable Visual Transformer for Skin Disease Detection

The clinical diagnosis of skin diseases is susceptible to interference from inter-class similarity of skin lesions, and over-reliance on clinicians'experience easily leads to subjective bias. Although existing deep learning aided diagnosis methods achieve competitive accuracy, they suffer from the black-box opacity of Vision Transformer (ViT) and poor adaptability to medical few-shot scenarios. Moreover, mainstream explainable algorithms generally face the bottleneck of significant accuracy degradation when improving interpretability. This paper proposes an interpretable ViT (IViT) constrained by Quadratic Programming (QP). The introduced pre-trained transfer learning adapts to few-shot feature extraction. A discrete QP feature selection framework is constructed to screen generic and discriminative features consistent with clinical diagnostic logic. A multi-objective loss function is designed to reduce feature redundancy and optimize activation distribution while preserving classification performance. Experimental results on six standard skin disease datasets show that IViT achieves an accuracy of 93.80%, only 0.21% lower than the baseline, with feature redundancy reduced by 29.5%. Its core activation regions are consistent with clinically concerned lesion areas. The proposed model balances accuracy and interpretability, providing a reliable solution for the clinical deployment of few-shot intelligent skin disease diagnosis.

eess.IV↗