Glioma Segmentation on Multicenter 2D-Based Magnetic Resonance Imaging Using Low-Rank Adaptation Tuning of a Foundation Model: An External Test Evaluation.

Impact of DWI quality and tumor complexity on deep learning-based segmentation of rectal cancer using multicenter MRI data

To assess the influence of DWI quality and tumor complexity on the performance of a fully automated deep learning segmentation algorithm in a large and heterogeneous clinical dataset of rectal cancer MRIs. In this retrospective multicenter study, nnU-Nets were trained on baseline staging MRIs using either a dataset of...

Parameter-Efficient pretrained-CT-to-MRI Transfer for Rectal Cancer Segmentation: Performance-Calibration Trade-offs

SWIFT, a SWin pretrained model wtih parameter-eFficient and tumor-aware fine-tuning for rectal cancer segmentation, is introduced, supporting robustness across pretrained initializations rather than external clinical generalizability.

An nnU-Net-based framework with adaptive feature representation for 3D brain tumor segmentation

CondSEB-Net improves adaptive feature representation and boundary-related segmentation performance while preserving the self-configuring pipeline of nnU-Net v2, and provides benchmark-level evidence for the proposed framework.

Zero-shot segmentation and tumor diameter measurement of soft tissue sarcomas on MRI using a multimodal large language model.

Region-Specific Attention-Enhanced U-Net: Architectural Optimization for Small-Target and Weak-Boundary Segmentation in TMJ MRI.

Bridging field strengths: fine-tuned deep learning models for 7T MRI white matter lesion segmentation in multiple sclerosis