bibtext: @InProceedings{ Liu_PAMIL_MICCAI2024,
                   author = { Liu, Jiashuai and Mao, Anyu and Niu, Yi and Zhang, Xianli and Gong, Tieliang and Li, Chen and Gao, Zeyu },
                   title = { { PAMIL: Prototype Attention-based Multiple Instance Learning for Whole Slide Image Classification } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: Digital pathology images are not only crucial for diagnosing cancer but also play a significant role in treatment planning, and research into disease mechanisms. The multiple instance learning (MIL) technique provides an effective weakly-supervised methodology for analyzing gigapixel Whole Slide Image (WSI). Recent advancements in MIL approaches have predominantly focused on predicting a singular diagnostic label for each WSI, simultaneously enhancing interpretability via attention mechanisms. However, given the heterogeneity of tumors, each WSI may contain multiple histotypes. Also, the generated attention maps often fail to offer a comprehensible explanation of the underlying reasoning process. These constraints limit the potential applicability of MIL-based methods in clinical settings. In this paper, we propose a Prototype Attention-based Multiple Instance Learning (PAMIL) method, designed to improve the model’s reasoning interpretability without compromising its classification performance at the WSI level. PAMIL merges prototype learning with attention mechanisms, enabling the model to quantify the similarity between prototypes and instances, thereby providing the interpretability at instance level. Specifically, two branches are equipped in PAMIL, providing prototype and instance-level attention scores, which are aggregated to derive bag-level predictions. Extensive experiments are conducted on four datasets with two diverse WSI classification tasks, demonstrating the effectiveness and interpretability of our PAMIL.
title: PAMIL: Prototype Attention-based Multiple Instance Learning for Whole Slide Image Classification
authors: Liu, Jiashuai Mao, Anyu Niu, Yi Zhang, Xianli Gong, Tieliang Li, Chen Gao, Zeyu