Post-pandemic healthcare for COVID-19 vaccine: Tissue-aware diagnosis of cervical lymphadenopathy via multi-modal ultrasound semantic segmentation.

With the widespread deployment of COVID-19 vaccines all around the world, billions of people have benefited from the vaccination and thereby avoiding infection. However, huge amount of clinical cases revealed diverse side effects of COVID-19 vaccines, among which cervical lymphadenopathy is one of the most frequent local reactions. Therefore, rapid detection of cervical lymph node (LN) is essential in terms of vaccine recipients' healthcare and avoidance of misdiagnosis in the post-pandemic era. This paper focuses on a novel deep learning-based framework for the rapid diagnosis of cervical lymphadenopathy towards COVID-19 vaccine recipients. Existing deep learning-based computer-aided diagnosis (CAD) methods for cervical LN enlargement mostly only depend on single modal images, e.g., grayscale ultrasound (US), color Doppler ultrasound, and CT, while failing to effectively integrate information from the multi-source medical images. Meanwhile, both the surrounding tissue objects of the cervical LNs and different regions inside the cervical LNs may imply valuable diagnostic knowledge which is pending for mining. In this paper, we propose an Tissue-Aware Cervical Lymph Node Diagnosis method (TACLND) via multi-modal ultrasound semantic segmentation. The method effectively integrates grayscale and color Doppler US images and realizes a pixel-level localization of different tissue objects, i.e., lymph, muscle, and blood vessels. With inter-tissue and intra-tissue attention mechanisms applied, our proposed method can enhance the implicit tissue-level diagnostic knowledge in both spatial and channel dimension, and realize diagnosis of cervical LN with normal, benign or malignant state. Extensive experiments conducted on our collected cervical LN US dataset demonstrate the effectiveness of our methods on both tissue detection and cervical lymphadenopathy diagnosis. Therefore, our proposed framework can guarantee efficient diagnosis for the vaccine recipients' cervical LN, and assist doctors to discriminate between COVID-related reactive lymphadenopathy and metastatic lymphadenopathy.

Predicting Organization Performance Changes: A Sequential Data-Based Framework.

The business environment is increasingly uncertain due to the rapid development of disruptive information technologies, the changing global economy, and the COVID-19 pandemic. This brings great uncertainties to investors to predict the performance changes and risks of companies. This research proposes a sequential data-based framework that aggregates data from multiple sources including both structured and unstructured data to predict the performance changes. It leverages data generated from the early risk warning system in China stock market to measure and predict organization performance changes based on the risk warning status changes of public companies. Different from the models in existing literature that focus on the prediction of risk warning of companies, our framework predicts a portfolio of organization performance changes, including business decline and recovery, thus helping investors to not only predict public company risks, but also discover investment opportunities. By incorporating sequential data, our framework achieves 92.3% macro-F1 value on real-world data from listed companies in China, outperforming other static models.