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Found 9 papers, 1 papers with code
Validation of the Practicability of Logical Assessment Formula for Evaluations with Inaccurate Ground-Truth Labels
Logical assessment formula (LAF) is a new theory proposed for evaluations with inaccurate ground-truth labels (IAGTLs) to assess the predictive models for various artificial intelligence applications.
Experts' cognition-driven ensemble deep learning for external validation of predicting pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer
The primary reason for this situation lies in that the distribution of the external data for validation is different from the distribution of the training data for the construction of the predictive model.
Experts' cognition-driven safe noisy labels learning for precise segmentation of residual tumor in breast cancer
Precise segmentation of residual tumor in breast cancer (PSRTBC) after neoadjuvant chemotherapy is a fundamental key technique in the treatment process of breast cancer.
One-Step Abductive Multi-Target Learning with Diverse Noisy Label Samples
One-step abductive multi-target learning (OSAMTL) was proposed to handle complex noisy labels.
Logical Assessment Formula and Its Principles for Evaluations with Inaccurate Ground-Truth Labels
Evaluations with accurate ground-truth labels (AGTLs) have been widely employed to assess predictive models for artificial intelligence applications.
One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancer
Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine learning and abductive learning, in inventing advanced artificial intelligence technologies.
A Survey on Ensemble Learning under the Era of Deep Learning
An urgent problem needs to be solved is how to take the significant advantages of ensemble deep learning while reduce the required expenses so that many more applications in specific fields can benefit from it.
Handling Noisy Labels via One-Step Abductive Multi-Target Learning and Its Application to Helicobacter Pylori Segmentation
Learning from noisy labels is an important concern in plenty of real-world scenarios.
Moderately Supervised Learning: Definition, Framework and Generality
In the current literature, by referring to the properties of the labels prepared for the training dataset, learning with supervision is categorized as supervised learning (SL) and weakly supervised learning (WSL).
