# Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning from Radiology Reports and Label Ontology

## TL;DR

This paper introduces LesaNet, a deep learning framework that holistically annotates diverse CT lesions with detailed labels by leveraging radiology reports and label ontology, significantly improving annotation accuracy.

## Contribution

The paper presents a novel lesion annotation network that incorporates hierarchical and mutually exclusive label relations, along with a label expansion and hard example mining strategies, to enhance multilabel prediction.

## Key findings

- Achieved an average AUC of 0.9344 on DeepLesion dataset.
- Effectively utilized label relations to improve annotation accuracy.
- Demonstrated the model's ability to produce comprehensive lesion labels.

## Abstract

In radiologists' routine work, one major task is to read a medical image, e.g., a CT scan, find significant lesions, and describe them in the radiology report. In this paper, we study the lesion description or annotation problem. Given a lesion image, our aim is to predict a comprehensive set of relevant labels, such as the lesion's body part, type, and attributes, which may assist downstream fine-grained diagnosis. To address this task, we first design a deep learning module to extract relevant semantic labels from the radiology reports associated with the lesion images. With the images and text-mined labels, we propose a lesion annotation network (LesaNet) based on a multilabel convolutional neural network (CNN) to learn all labels holistically. Hierarchical relations and mutually exclusive relations between the labels are leveraged to improve the label prediction accuracy. The relations are utilized in a label expansion strategy and a relational hard example mining algorithm. We also attach a simple score propagation layer on LesaNet to enhance recall and explore implicit relation between labels. Multilabel metric learning is combined with classification to enable interpretable prediction. We evaluated LesaNet on the public DeepLesion dataset, which contains over 32K diverse lesion images. Experiments show that LesaNet can precisely annotate the lesions using an ontology of 171 fine-grained labels with an average AUC of 0.9344.