The study investigates whether a deep learning segmentation model trained on adult kidney MRI can generalize to pediatric Wilms tumors. Trained on single‑sequence T1 or T2 adult MRI, the model achieved a median Dice similarity coefficient (DSC) of 0.86 on an adult test set and 0.85 on a pediatric cohort, compared to 0.90 for a pediatric‑specific nnU‑Net. Although the adult‑trained model performed well, the modest performance gap indicates that pediatric training data remain valuable.
arXiv:2505. 07573v2 Announce Type: replace-cross Abstract: Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments.
NeuroTS-Net is a 3‑D encoder‑decoder CNN designed for multi‑class semantic segmentation of pediatric brain tumors in multi‑modal MRI. It uses a dual‑scale raw‑detail stream, adaptive low‑resolution context selection, and detail‑preserving multipath downsampling to maintain fine intensity and boundary information while modeling broader tumor context. Trained on the BraTS 2026 pediatric dataset, it outperformed nnU‑Net and MedNeXt, achieving Dice scores of 0.938/0.937 on internal validation and 0.927/0.926 on the official challenge set.
arXiv:2609.21698v1 Announce Type: new Abstract: Deep learning segmentation of renal tumors requires large annotated datasets, yet clinical deployments typically offer only a handful of tumor-positive...
arXiv:2606. 04365v1 Announce Type: cross Abstract: Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level.
The paper introduces Pancreas‑MedNeXt, a unified MRI segmentation model that simultaneously delineates the entire pancreas, its subregions, pancreatic tumors, and surrounding organs at risk (OARs) from a single MRI volume. Using a shared MedNeXt encoder‑decoder and an annotation‑aware loss, the model is trained on partially labeled data and fine‑tuned for multitask output heads. In cross‑validation on 142 PANTHER patients and external test sets, Pancreas‑MedNeXt outperforms state‑of‑the‑art methods, achieving higher Dice similarity coefficients for pancreas, tumor, and OARs. "whyItMatters":"The model’s superior performance and unified approach could streamline contouring workflows for pancreatic cancer radiotherapy, potentially improving treatment planning efficiency."
arXiv:2608.30458v1 Announce Type: new Abstract: Accurate bladder tumor segmentation and assessment of mus- cle invasion from T2-weighted MRI are important for treatment plan- ning, but developing rob...
arXiv:2608.29944v1 Announce Type: new Abstract: Brain tumor segmentation in magnetic resonance imaging (MRI) is a critical task for diagnosis and treatment planning. Despite the success of deep learn...
The study explores how adding anatomical priors and active learning can improve the accuracy of deep learning models for segmenting the Clinical Target Volume (CTV) in gastric cancer radiotherapy. Using 100 retrospective CT scans, an nnU‑Net model trained on 10 expert‑contoured cases was enhanced with voxel‑wise anatomical prior maps and iterative active learning over four rounds. The combined approach raised the mean Dice Similarity Coefficient from 0.84 to 0.87, demonstrating that both techniques individually and together improve segmentation performance and generalizability.
arXiv:2604.27697v2 Announce Type: replace-cross Abstract: Peritoneal metastases (PM) are staged using the surgically determined Peritoneal Cancer Index (sPCI), which requires invasive laparoscopic as...
The paper presents the first systematic evaluation of out‑of‑distribution generalization for congenital heart disease (CHD) segmentation, using the ImageCHD cohort as a held‑out target. It compares several segmentation architectures under different training regimes, showing that in‑distribution performance is a poor predictor of cross‑cohort robustness: nnU‑Net drops from 0.77 to 0.51 Dice, while SwinUNETR maintains higher performance at 0.67 Dice. Limited target‑domain adaptation with only 11 labeled ImageCHD cases boosts all SwinUNETR variants above 0.76 Dice, highlighting the importance of explicit cross‑dataset testing.
This study presents a clinically relevant framework for evaluating deep neural networks that segment lymphoma lesions in PET/CT images, addressing gaps such as out‑of‑distribution testing and comparison with expert annotators. Using 611 multi‑institutional cases, the authors assess four networks (ResUNet, SegResNet, DynUNet, SwinUNETR) with lesion‑specific metrics, detection criteria, and metabolic‑characteristic‑based thresholds, finding that models perform best on large, intense lesions. The work also demonstrates that network errors mirror those of physicians, highlighting shared challenges with small, faint lesions.
This study introduces a computationally efficient convolutional neural network (CNN) architecture enhanced with transfer learning for multi-cancer detection using biomedical images. The proposed lightweight CNN model is designed to reduce computational complexity while maintaining high classification performance, making it suitable for deployment in resource-constrained environments.