An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data.

Zhao H, Miao C, Zhu Y, Shu Y, Wu X, Yin Z, Deng X, Gong W, Yang Z, Zou W

Open source

DOI
10.1186/s12885-025-14462-9
Published
2025 Jul 16
Container
BMC cancer
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.1186/s12885-025-14462-9,
  title = {An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data.},
  author = {Zhao H and Miao C and Zhu Y and Shu Y and Wu X and Yin Z and Deng X and Gong W and Yang Z and Zou W},
  year = {2025},
  journal = {BMC cancer},
  doi = {10.1186/s12885-025-14462-9},
  url = {https://doi.org/10.1186/s12885-025-14462-9}
}

RIS

TY  - JOUR
TI  - An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data.
AU  - Zhao H
AU  - Miao C
AU  - Zhu Y
AU  - Shu Y
AU  - Wu X
AU  - Yin Z
AU  - Deng X
AU  - Gong W
AU  - Yang Z
AU  - Zou W
PY  - 2025
JO  - BMC cancer
DO  - 10.1186/s12885-025-14462-9
UR  - https://doi.org/10.1186/s12885-025-14462-9
ER  -

APA

H, Z., C, M., Y, Z., Y, S., X, W., Z, Y., X, D., W, G., Z, Y., & W, Z. (2025). An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data.. BMC cancer. https://doi.org/10.1186/s12885-025-14462-9

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