Esophageal cancer is one of the most common types of cancer worldwide and
ranks sixth in cancer-related mortality. Accurate computer-assisted diagnosis
of cancer progression can help physicians effectively customize personalized
treatment plans. Currently, CT-based cancer diagnosis methods have received
much attention for their comprehensive ability to examine patients' conditions.
However, multi-modal based methods may likely introduce information redundancy,
leading to underperformance. In addition, efficient and effective interactions
between multi-modal representations need to be further explored, lacking
insightful exploration of prognostic correlation in multi-modality features. In
this work, we introduce a multi-modal heterogeneous graph-based conditional
feature-guided diffusion model for lymph node metastasis diagnosis based on CT
images as well as clinical measurements and radiomics data. To explore the
intricate relationships between multi-modal features, we construct a
heterogeneous graph. Following this, a conditional feature-guided diffusion
approach is applied to eliminate information redundancy. Moreover, we propose a
masked relational representation learning strategy, aiming to uncover the
latent prognostic correlations and priorities of primary tumor and lymph node
image representations. Various experimental results validate the effectiveness
of our proposed method. The code is available at
this https URL

介绍了一种基于 CT 图像以及临床测量和放射学数据的多模式异质图条件特征引导扩散模型，用于淋巴结转移诊断。通过构建异质图和应用条件特征引导扩散方法来消除信息冗余，并提出了一种掩蔽关系表示学习策略，旨在揭示原发肿瘤和淋巴结图像表示的潜在预后相关性和优先级。通过各种实验结果验证了我们提出方法的有效性。

MMFusion：食管癌淋巴结转移诊断的多模态扩散模型

MMFusion: Multi-modality Diffusion Model for Lymph Node Metastasis  Diagnosis in Esophageal Cancer

Lymph node metastasis is one of the most significant diagnostic indicators in
breast cancer, which is traditionally observed under the microscope by
pathologists. In recent years, computerized histology diagnosis has become one
of the most rapidly expanding fields in medical image computing, which
alleviates pathologists' workload and reduces misdiagnosis rate. However,
automatic detection of lymph node metastases from whole slide images remains a
challenging problem, due to the large-scale data with enormous resolutions and
existence of hard mimics. In this paper, we propose a novel framework by
leveraging fully convolutional networks for efficient inference to meet the
speed requirement for clinical practice, while reconstructing dense predictions
under different offsets for ensuring accurate detection on both micro- and
macro-metastases. Incorporating with the strategies of asynchronous sample
prefetching and hard negative mining, the network can be effectively trained.
Extensive experiments on the benchmark dataset of 2016 Camelyon Grand Challenge
corroborated the efficacy of our method. Compared with the state-of-the-art
methods, our method achieved superior performance with a faster speed on the
tumor localization task and surpassed human performance on the WSI
classification task.

本文研究了利用卷积神经网络的计算机诊断技术在乳腺癌病理学中的应用，从组织样本的全幅图像中快速自动检测淋巴结转移的新方法，并且在 2016 年 Camelyon Grand Challenge 的数据集上取得了优异的性能，不仅比其他同类方法检测肿瘤定位的速度更快，而且在 WSI 分类任务上超越了人类表现。