Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer.
In Quantitative imaging in medicine and surgery
BACKGROUND :
METHODS :
RESULTS :
CONCLUSIONS :
Feng Min, Zhao Yu, Chen Jie, Zhao Tingyu, Mei Juan, Fan Yingying, Lin Zhenyu, Yao Jianhua, Bu Hong
2023-Mar-01
Endometrial cancer (EC), deep learning model, lymph node metastasis (LNM), prediction
The value of using a deep learning image reconstruction algorithm of thinner slice thickness to balance the image noise and spatial resolution in low-dose abdominal CT.
In Quantitative imaging in medicine and surgery
BACKGROUND :
METHODS :
RESULTS :
CONCLUSIONS :
Wang Huan, Li Xinyu, Wang Tianze, Li Jianying, Sun Tianze, Chen Lihong, Cheng Yannan, Jia Xiaoqian, Niu Xinyi, Guo Jianxin
2023-Mar-01
Deep learning, different layer thickness, image reconstruction, radiation dose

Novel estimation technique for the carrier-to-noise ratio of wireless medical telemetry using software-defined radio with machine-learning.
In Scientific reports ; h5-index 158.0
Kai Ishida
2023-Mar-13

The predictive model for COVID-19 pandemic plastic pollution by using deep learning method.
In Scientific reports ; h5-index 158.0
Nanehkaran Yaser A, Licai Zhu, Azarafza Mohammad, Talaei Sona, Jinxia Xu, Chen Junde, Derakhshani Reza
2023-Mar-13
FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization
ArXiv Preprint
Qianqian Xie, Jinpeng Hu, Jiayu Zhou, Yifan Peng, Fei Wang
2023-03-15

UMRFormer-net: a three-dimensional U-shaped pancreas segmentation method based on a double-layer bridged transformer network.
In Quantitative imaging in medicine and surgery
BACKGROUND :
METHODS :
RESULTS :
CONCLUSIONS :
Fang Kun, He Baochun, Liu Libo, Hu Haoyu, Fang Chihua, Huang Xuguang, Jia Fucang
2023-Mar-01
Pancreas, U-Net, deep learning, image segmentation, transformer
Weekly Summary
Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.