Abstract
Shuwen Yang, Fuyong Sui, Zhuang Liang, Qilin Liu, Binyang Fu
Abstract
Authors
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10.2307/2531595 · doi-reference
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SMOTE: synthetic minority over-sampling technique
10.1613/jair.953 · doi-reference
A guide to cross-validation for artificial intelligence in medical imaging
10.1148/ryai.220232 · doi-reference
Perineural invasion predicts poor survival and cervical lymph node metastasis in oral squamous cell carcinoma
10.4317/medoral.25916 · doi-reference
Prognostic value of perineural invasion on survival and recurrence in oral squamous cell carcinoma
10.3390/diagnostics12051062 · doi-reference
Perineural invasion is a significant prognostic factor in oral squamous cell carcinoma: a systematic review and meta-analysis
10.3390/diagnostics13213339 · doi-reference
Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis
10.3389/fonc.2024.1395159 · doi-reference
Identification of CT-based non-invasive radiomic biomarkers for overall survival prediction in oral cavity squamous cell carcinoma
10.1038/s41598-023-48048-x · doi-reference
The role of magnetic resonance imaging and computed tomography in oral squamous cell carcinoma patients’ preoperative staging
10.3389/fonc.2023.972042 · doi-reference
Computational radiomics system to decode the radiographic phenotype
10.1158/0008-5472.can-17-0339 · doi-reference
Quantification of the immune content in neuroblastoma: deep learning and topological data analysis in digital pathology
10.3390/ijms22168804 · doi-reference
Improving feature extraction from histopathological images through a fine-tuning ImageNet model
10.1016/j.jpi.2022.100115 · doi-reference
Transfer learning for medical image classification: a literature review
10.1186/s12880-022-00793-7 · doi-reference
TIAToolbox as an end-to-end library for advanced tissue image analytics
10.1038/s43856-022-00186-5 · doi-reference
Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer
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Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer
10.1038/s43018-022-00388-9 · doi-reference
Multimodal data fusion for cancer biomarker discovery with deep learning
10.1038/s42256-023-00633-5 · doi-reference
Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis
10.1109/tmi.2020.3021387 · doi-reference
Multimodal fusion model for prognostic prediction and radiotherapy response assessment in head and neck squamous cell carcinoma
10.1038/s41746-025-01712-0 · doi-reference
Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics
10.3389/fmed.2023.1217037 · doi-reference
Radiomics applications in head and neck tumor imaging: a narrative review
10.3390/cancers15041174 · doi-reference
Integrative prognostic modeling and mediation analysis of recurrence risk in extremely early-stage oral squamous cell carcinoma
10.1016/j.jormas.2025.102708 · doi-reference
Identification of genomic alteration and prognosis using pathomics-based artificial intelligence in oral leukoplakia and head and neck squamous cell carcinoma: a multicenter experimental study
10.1097/js9.0000000000002077 · doi-reference
Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists
10.1038/s41598-023-38343-y · doi-reference