ArticleInsights into imaging2023
Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI.
Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Deep Learning and Machine Learning Algorithms for Cervical Cancer Segmentation on MRI: A Systematic Review.Journal of medical radiation sciences · 2026Review
- Application of TransUnet Deep Learning Model for Automatic Segmentation of Cervical Cancer in Small-Field T2WI Images.Journal of imaging informatics in medicine · 2025Article
- RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models.Journal of imaging informatics in medicine · 2025Article
- Cer-ConvN3Unet: an end-to-end multi-parametric MRI-based pipeline for automated detection and segmentation of cervical cancer.European radiology experimental · 2025Article
- MVT-Net: A novel cervical tumour segmentation using multi-view feature transfer learning.PloS one · 2025Article
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
- Endometrial cancer risk stratification using MRI radiomics: corroborating with choline metabolism.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Cervical cancer segmentation based on medical images: a literature review.Quantitative imaging in medicine and surgery · 2024Review
- Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2023Article
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11 authors.
Funding
Abstract
purposeTo investigate the generalizability of transfer learning (TL) of automated tumor segmentation from cervical cancers toward a universal model for cervical and uterine malignancies in diffusion-weighted magnetic resonance imaging (DWI).
methodsIn this retrospective multicenter study, we analyzed pelvic DWI data from 169 and 320 patients with cervical and uterine malignancies and divided them into the training (144 and 256) and testing (25 and 64) datasets, respectively. A pretrained model was established using DeepLab V3 + from the cervical cancer dataset, followed by TL experiments adjusting the training data sizes and fine-tuning layers. The model performance was evaluated using the dice similarity coefficient (DSC).
resultsIn predicting tumor segmentation for all cervical and uterine malignancies, TL models improved the DSCs from the pretrained cervical model (DSC 0.43) when adding 5, 13, 26, and 51 uterine cases for training (DSC improved from 0.57, 0.62, 0.68, 0.70, p < 0.001). Following the crossover at adding 128 cases (DSC 0.71), the model trained by combining data from adding all the 256 patients exhibited the highest DSCs for the combined cervical and uterine datasets (DSC 0.81) and cervical only dataset (DSC 0.91).
conclusionsTL may improve the generalizability of automated tumor segmentation of DWI from a specific cancer type toward multiple types of uterine malignancies especially in limited case numbers.
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