Multi-modal Learning has attracted widespread attention in medical image
analysis. Using multi-modal data, whole slide images (WSIs) and clinical
information, can improve the performance of deep learning models in the
diagnosis of axillary lymph node metastasis. However, clinical information is
not easy to collect in clinical practice due to privacy concerns, limited
resources, lack of interoperability, etc. Although patient selection can ensure
the training set to have multi-modal data for model development, missing
modality of clinical information can appear during test. This normally leads to
performance degradation, which limits the use of multi-modal models in the
clinic. To alleviate this problem, we propose a bidirectional distillation
framework consisting of a multi-modal branch and a single-modal branch. The
single-modal branch acquires the complete multi-modal knowledge from the
multi-modal branch, while the multi-modal learns the robust features of WSI
from the single-modal. We conduct experiments on a public dataset of Lymph Node
Metastasis in Early Breast Cancer to validate the method. Our approach not only
achieves state-of-the-art performance with an AUC of 0.861 on the test set
without missing data, but also yields an AUC of 0.842 when the rate of missing
modality is 80\%. This shows the effectiveness of the approach in dealing with
multi-modal data and missing modality. Such a model has the potential to
improve treatment decision-making for early breast cancer patients who have
axillary lymph node metastatic status.

基于多模态学习的鉴别性淋巴结转移诊断模型，在临床医学实践中，通过双向蒸馏框架获得全面的多模态知识和稳健的 WSI 特征，有效地处理多模态数据和丢失模态，提高早期乳腺癌患者治疗决策的准确性。

预测腋窝淋巴结转移中的缺失模态下的多模态学习

Multi-modal Learning with Missing Modality in Predicting Axillary Lymph  Node Metastasis

Breast cancer is a major concern for women's health globally, with axillary
lymph node (ALN) metastasis identification being critical for prognosis
evaluation and treatment guidance. This paper presents a deep learning (DL)
classification pipeline for quantifying clinical information from digital
core-needle biopsy (CNB) images, with one step less than existing methods. A
publicly available dataset of 1058 patients was used to evaluate the
performance of different baseline state-of-the-art (SOTA) DL models in
classifying ALN metastatic status based on CNB images. An extensive ablation
study of various data augmentation techniques was also conducted. Finally, the
manual tumor segmentation and annotation step performed by the pathologists was
assessed.

使用深度学习来识别乳腺癌患者核心 - 针生物检查图像中腋窝淋巴结转移的状态，并通过评估性能和进行数据增强技术的研究来改进现有的方法。