372 citations · 448 across the 11 of their papers we have counts for
11 papers
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…
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…
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…
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…
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…
One-Step Abductive Multi-Target Learning with Diverse Noisy Label Samples
Yongquan Yang
One-step abductive multi-target learning (OSAMTL) was proposed to handle complex noisy labels. In this paper, giving definition of diverse noisy label samples (DNLS), we propose on…
Logical Assessment Formula and Its Principles for Evaluations with Inaccurate Ground-Truth Labels
Yongquan Yang
Evaluations with accurate ground-truth labels (AGTLs) have been widely employed to assess predictive models for artificial intelligence applications. However, in some specific fiel…
One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancer
Yongquan Yang, Fengling Li, Yani Wei +4
Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine l…
A Survey on Ensemble Learning under the Era of Deep Learning
Yongquan Yang, Haijun Lv, Ning Chen
Due to the dominant position of deep learning (mostly deep neural networks) in various artificial intelligence applications, recently, ensemble learning based on deep neural networ…
Handling Noisy Labels via One-Step Abductive Multi-Target Learning and Its Application to Helicobacter Pylori Segmentation
Yongquan Yang, Yiming Yang, Jie Chen +2
Learning from noisy labels is an important concern in plenty of real-world scenarios. Various approaches for this concern first make corrections corresponding to potentially noisy-…
Moderately Supervised Learning: Definition, Framework and Generality
Yongquan Yang
Learning with supervision has achieved remarkable success in numerous artificial intelligence (AI) applications. In the current literature, by referring to the properties of the la…