From the 1 of 5 linked papers with an AI index.
5 papers
Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning
Wei Tang, Yin-Fang Yang, Weijia Zhang +1
The paper introduces a calibratable disambiguation loss (CDL) that improves both classification accuracy and confidence calibration for multi-instance partial-label learning by inc…
From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classification
Xin Liu, Weijia Zhang, Wei Tang +4
In whole slide images (WSIs) analysis, attention-based multi-instance learning (MIL) models are susceptible to spurious correlations and degrade under domain shift. These methods m…
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks
Xin Liu, Weijia Zhang, Min-Ling Zhang
In survival analysis, subjects often face competing risks; for example, individuals with cancer may also suffer from heart disease or other illnesses, which can jointly influence t…
Multi-Instance Partial-Label Learning with Margin Adjustment
Wei Tang, Yin-Fang Yang, Zhaofei Wang +2
Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label s…
Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning
Wei Tang, Weijia Zhang, Min-Ling Zhang
Multi-instance partial-label learning (MIPL) addresses scenarios where each training sample is represented as a multi-instance bag associated with a candidate label set containing…