Towards Interpretable Anomaly Detection via Invariant Rule Mining

In the research area of anomaly detection, novel and promising methods are frequently developed. However, most existing studies, especially those leveraging deep neural networks, exclusively focus on the detection task only and ignore the interpretability of the underlying models as well as their detection results. However, anomaly interpretation, which aims to provide explanation of why specific data instances are identified as anomalies, is an equally (if not more) important task in many real-world applications. In this work, we pursue highly interpretable anomaly detection via invariant rule mining. Specifically, we leverage decision tree learning and association rule mining to automatically generate invariant rules that are consistently satisfied by the underlying data generation process. The generated invariant rules can provide explicit explanation of anomaly detection results and thus are extremely useful for subsequent decision-making. Furthermore, our empirical evaluation shows that the proposed method can also achieve comparable performance in terms of AUC and partial AUC with popular anomaly detection models in various benchmark datasets.

Segmentation Network with Compound Loss Function for Hydatidiform Mole Hydrops Lesion Recognition

Pathological morphology diagnosis is the standard diagnosis method of hydatidiform mole. As a disease with malignant potential, the hydatidiform mole section of hydrops lesions is an important basis for diagnosis. Due to incomplete lesion development, early hydatidiform mole is difficult to distinguish, resulting in a low accuracy of clinical diagnosis. As a remarkable machine learning technology, image semantic segmentation networks have been used in many medical image recognition tasks. We developed a hydatidiform mole hydrops lesion segmentation model based on a novel loss function and training method. The model consists of different networks that segment the section image at the pixel and lesion levels. Our compound loss function assign weights to the segmentation results of the two levels to calculate the loss. We then propose a stagewise training method to combine the advantages of various loss functions at different levels. We evaluate our method on a hydatidiform mole hydrops dataset. Experiments show that the proposed model with our loss function and training method has good recognition performance under different segmentation metrics.

A Semantic Segmentation Network Based Real-Time Computer-Aided Diagnosis System for Hydatidiform Mole Hydrops Lesion Recognition in Microscopic View

As a disease with malignant potential, hydatidiform mole (HM) is one of the most common gestational trophoblastic diseases. For pathologists, the HM section of hydrops lesions is an important basis for diagnosis. In pathology departments, the diverse microscopic manifestations of HM lesions and the limited view under the microscope mean that physicians with extensive diagnostic experience are required to prevent missed diagnosis and misdiagnosis. Feature extraction can significantly improve the accuracy and speed of the diagnostic process. As a remarkable diagnosis assisting technology, computer-aided diagnosis (CAD) has been widely used in clinical practice. We constructed a deep-learning-based CAD system to identify HM hydrops lesions in the microscopic view in real-time. The system consists of three modules; the image mosaic module and edge extension module process the image to improve the outcome of the hydrops lesion recognition module, which adopts a semantic segmentation network, our novel compound loss function, and a stepwise training function in order to achieve the best performance in identifying hydrops lesions. We evaluated our system using an HM hydrops dataset. Experiments show that our system is able to respond in real-time and correctly display the entire microscopic view with accurately labeled HM hydrops lesions.