5 papers
Negative Ontology of True Target for Machine Learning: Towards Recognition, Evaluation and Learning under Democratic Supervision
Yongquan Yang
This article philosophically examines how a shift in the assumed ontology of the true target (TT) can lead to a new paradigm for machine learning (ML)-based predictive modelling. B…
LAF-Based Evaluation and UTTL-Based Learning Strategies with MIATTs
Yongquan Yang
In many real-world machine learning (ML) applications, the true target cannot be precisely defined due to ambiguity or subjectivity information. To address this challenge, under th…
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…
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…
Validation of the Practicability of Logical Assessment Formula for Evaluations with Inaccurate Ground-Truth Labels: An Application Study on Tumour Segmentation for Breast Cancer
Yongquan Yang, Hong Bu
The logical assessment formula (LAF) is a new theory proposed for evaluations with inaccurate ground-truth labels (IAGTLs) to assess the predictive models for artificial intelligen…