Zobrazeno 1 - 10
of 510
pro vyhledávání: '"Jiang, Jue"'
Self-supervised learning (SSL) is an approach to extract useful feature representations from unlabeled data, and enable fine-tuning on downstream tasks with limited labeled examples. Self-pretraining is a SSL approach that uses the curated task datas
Externí odkaz:
http://arxiv.org/abs/2405.08657
Autor:
Lin, Xun, Yu, Yi, Xia, Song, Jiang, Jue, Wang, Haoran, Yu, Zitong, Liu, Yizhong, Fu, Ying, Wang, Shuai, Tang, Wenzhong, Kot, Alex
The widespread availability of publicly accessible medical images has significantly propelled advancements in various research and clinical fields. Nonetheless, concerns regarding unauthorized training of AI systems for commercial purposes and the du
Externí odkaz:
http://arxiv.org/abs/2403.14250
We assessed the trustworthiness of two self-supervision pretrained transformer models, Swin UNETR and SMIT, for fine-tuned lung (LC) tumor segmentation using 670 CT and MRI scans. We measured segmentation accuracy on two public 3D-CT datasets, robust
Externí odkaz:
http://arxiv.org/abs/2403.13113
Pretraining vision transformers (ViT) with attention guided masked image modeling (MIM) has shown to increase downstream accuracy for natural image analysis. Hierarchical shifted window (Swin) transformer, often used in medical image analysis cannot
Externí odkaz:
http://arxiv.org/abs/2310.01209
Publikováno v:
Cell Stress & Chaperones, 2023 Nov 01. 28(6), 989-999.
Externí odkaz:
https://www.jstor.org/stable/48759922
Autor:
Simeth, Josiah, Jiang, Jue, Nosov, Anton, Wibmer, Andreas, Zelefsky, Michael, Tyagi, Neelam, Veeraraghavan, Harini
Publikováno v:
Med Phys. 2023 Mar 1
Dose escalation radiotherapy allows increased control of prostate cancer (PCa) but requires segmentation of dominant index lesions (DIL), motivating the development of automated methods for fast, accurate, and consistent segmentation of PCa DIL. We e
Externí odkaz:
http://arxiv.org/abs/2303.03494
Autor:
Jiang, Jue, Hong, Jun, Tringale, Kathryn, Reyngold, Marsha, Crane, Christopher, Tyagi, Neelam, Veeraraghavan, Harini
Method: ProRSeg was trained using 5-fold cross-validation with 110 T2-weighted MRI acquired at 5 treatment fractions from 10 different patients, taking care that same patient scans were not placed in training and testing folds. Segmentation accuracy
Externí odkaz:
http://arxiv.org/abs/2210.14297
Vision transformers, with their ability to more efficiently model long-range context, have demonstrated impressive accuracy gains in several computer vision and medical image analysis tasks including segmentation. However, such methods need large lab
Externí odkaz:
http://arxiv.org/abs/2205.10342
Autor:
Jiang, Jue, Veeraraghavan, Harini
Image-guided adaptive lung radiotherapy requires accurate tumor and organs segmentation from during treatment cone-beam CT (CBCT) images. Thoracic CBCTs are hard to segment because of low soft-tissue contrast, imaging artifacts, respiratory motion, a
Externí odkaz:
http://arxiv.org/abs/2201.11000
Autor:
Zheng, Fangyuan, Fan, Shangchun, Wei, Yihang, Wang, Zihao, Wei, Xiaorui, Borjigin, Wonbayar, Jiang, Jue, Qu, Xiaolei
Publikováno v:
In Measurement 31 May 2024 231