output
20202025
most citedPreliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019-nCoV

404 citations

12 papers

cs.LG2025★ 2 cited

Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer Prognosis

Yonghao Zhao, Changtao Li, Chi Shu +7

Survival prognosis is crucial for medical informatics. Practitioners often confront small-sized clinical data, especially cancer patient cases, which can be insufficient to induce…

eess.IV2024★ 9 cited

NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation

Zhenye Lou, Qing Xu, Zekun Jiang +6

Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image…

q-bio.QM2024★ 28 cited

Advancing bioinformatics with large language models: components, applications and perspectives

Jiajia Liu, Mengyuan Yang, Yankai Yu +4

Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language pro…

physics.med-ph2023★ 4 cited

Experts' cognition-driven ensemble deep learning for external validation of predicting pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer

Yongquan Yang, Fengling Li, Yani Wei +4

In breast cancer, neoadjuvant chemotherapy (NAC) provides a standard treatment option for patients who have locally advanced cancer and some large operable tumors. A patient will h…

q-bio.QM2023

Experts' cognition-driven safe noisy labels learning for precise segmentation of residual tumor in breast cancer

Yongquan Yang, Jie Chen, Yani Wei +2

Precise segmentation of residual tumor in breast cancer (PSRTBC) after neoadjuvant chemotherapy is a fundamental key technique in the treatment process of breast cancer. However, a…

eess.IV2022★ 64 cited

PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation

Guotai Wang, Xiangde Luo, Ran Gu +6

Background and Objective: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models. Existing toolkits mainly focus on fully…