Jiayi Zhang
Suzhou, 215163, China
Last Updated: 09/26/2026
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Found 12 publications
DCBLD1 Promotes Lung Tumorigenesis by Inhibiting PTP1B Dephosphorylation of EGFR.
Journal: International journal of biological sciences
Published: February 13, 2025
Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study.
Journal: Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Published: May 19, 2022
Deep learning-based fully automated diagnosis of melanocytic lesions by using whole slide images.
Journal: The Journal of dermatological treatment
Published: February 03, 2022
Deep learning-based fully automated differential diagnosis of eyelid basal cell and sebaceous carcinoma using whole slide images.
Journal: Quantitative imaging in medicine and surgery
Published: January 28, 2022
A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study.
Journal: EClinicalMedicine
Published: December 31, 2021
MRI-Based Multiple Instance Convolutional Neural Network for Increased Accuracy in the Differentiation of Borderline and Malignant Epithelial Ovarian Tumors.
Journal: Journal of magnetic resonance imaging : JMRI
Published: September 13, 2021
3D multi-scale, multi-task, and multi-label deep learning for prediction of lymph node metastasis in T1 lung adenocarcinoma patients' CT images.
Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Published: June 05, 2021
CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy.
Journal: European radiology
Published: October 12, 2020
Multiple instance convolutional neural network with modality-based attention and contextual multi-instance learning pooling layer for effective differentiation between borderline and malignant epithelial ovarian tumors.
Journal: Artificial intelligence in medicine
Published: October 02, 2020
Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram.
Journal: European radiology
Published: September 20, 2020
MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers.
Journal: European radiology
Published: May 26, 2020
MRI-Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study.
Journal: Journal of magnetic resonance imaging : JMRI
Published: November 20, 2019
Showing 1-12 of 12
Last Updated: 09/26/2026