bibtext: @InProceedings{ Cui_Multilevel_MICCAI2024,
                   author = { Cui, Xiaoxiao and Jiang, Shanzhi and Sun, Baolin and Li, Yiran and Cao, Yankun and Li, Zhen and Lv, Chaoyang and Liu, Zhi and Cui, Lizhen and Li, Shuo },
                   title = { { Multilevel Causality Learning for Multi-label Gastric Atrophy Diagnosis } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15003 },
                   month = {October},
                   pages = { pending },
                }
abstract: No studies have formulated endoscopic grading (EG) of gastric atrophy (GA) as a multi-label classification (MLC) problem, which requires the simultaneous detection of GA and its gastric sites during an endoscopic examination. Accurate EG of GA is crucial for assessing the progression of early gastric cancer. However, the strong visual interference in endoscopic images is caused by various inter-image differences and subtle intra-image differences, leading to confounding contexts and hindering the causalities between class-aware features (CAFs) and multi-label predictions. We propose a multilevel causality learning approach for multi-label gastric atrophy diagnosis for the first time, to learn robust causal CAFs by de-confounding multilevel confounders. Our multilevel causal model is built based on a transformer to construct a multilevel confounder set and implement a progressive causal intervention (PCI) on it. Specifically, the confounder set is constructed by a dual token path sampling module that leverages multiple class tokens and different hidden states of patch tokens to stratify various visual interference. PCI involves attention-based sample-level re-weighting and uncertainty-guided logit-level modulation. Comparative experiments on an endoscopic dataset demonstrate the significant improvement of our model, such as IDA (0.95% on OP, and 0.65% on mAP) and TS-Former (1.11% on OP, and 1.05% on mAP).
  \keywords{Multi-label Classification  \and Causal Intervention \and Gastric Atrophy Detection.}
title: Multilevel Causality Learning for Multi-label Gastric Atrophy Diagnosis
authors: Cui, Xiaoxiao Jiang, Shanzhi Sun, Baolin Li, Yiran Cao, Yankun Li, Zhen Lv, Chaoyang Liu, Zhi Cui, Lizhen Li, Shuo