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

Fine-tuning is an effective and practical strategy for adapting deep learning WML segmentation algorithms to 7T MRI, even with limited annotated data, and may be included in MS research workflows to facilitate multi-center collaborations with 7T MRI.