Bangkang Fu

Orcid: 0000-0001-7647-3371

According to our database1, Bangkang Fu authored at least 14 papers between 2022 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2026
HSFSurv: A hybrid supervision framework at individual and feature levels for multimodal cancer survival analysis.
Medical Image Anal., 2026

Advancing automated diagnosis of active pulmonary tuberculosis in multi-center settings through domain generalization and multi-scale convolution fusion.
Biomed. Signal Process. Control., 2026

2025
RPF-Net: A multimodal model for the postoperative UISS risk stratification of non-metastatic ccRCC based on CT and whole-slide images.
Comput. Methods Programs Biomed., 2025

BMA-Net: A 3D bidirectional multi-scale feature aggregation network for prostate region segmentation.
Comput. Methods Programs Biomed., 2025

Unveiling hidden risks: A Holistically-Driven Weak Supervision framework for ultra-short-term ACS prediction using CCTA.
Comput. Medical Imaging Graph., 2025

GAMMIL: A graph attention-guided multi-scale fusion multiple instance learning model for the WHO grading of meningioma in whole slide images.
Biomed. Signal Process. Control., 2025

Segmentation outperforms registration in quantitative analysis of brain iron.
Biomed. Signal Process. Control., 2025

ACE-QSM: Accelerating quantitative susceptibility mapping acquisition using diffusion models by reducing repetition time.
Biomed. Signal Process. Control., 2025

2024
HmsU-Net: A hybrid multi-scale U-net based on a CNN and transformer for medical image segmentation.
Comput. Biol. Medicine, March, 2024

SaB-Net: Self-attention backward network for gastric tumor segmentation in CT images.
Comput. Biol. Medicine, February, 2024

mQSM: Multitask Learning-Based Quantitative Susceptibility Mapping for Iron Analysis in Brain.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

2023
TGMIL: A hybrid multi-instance learning model based on the Transformer and the Graph Attention Network for whole-slide images classification of renal cell carcinoma.
Comput. Methods Programs Biomed., December, 2023

msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping.
NeuroImage, 2023

2022
StoHisNet: A hybrid multi-classification model with CNN and Transformer for gastric pathology images.
Comput. Methods Programs Biomed., 2022


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