# MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation

## TL;DR

MULAN is a multitask neural network that jointly detects, tags, and segments lesions across the whole body in CT scans, significantly advancing automated lesion analysis.

## Contribution

It introduces a novel multitask framework based on Mask R-CNN with 3D feature fusion for comprehensive lesion analysis in medical imaging.

## Key findings

- Achieves state-of-the-art accuracy on DeepLesion dataset
- Joint task learning improves detection and tagging performance
- Tag predictions enhance detection accuracy through score refinement

## Abstract

When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report. To automate this process, we propose a multitask universal lesion analysis network (MULAN) for joint detection, tagging, and segmentation of lesions in a variety of body parts, which greatly extends existing work of single-task lesion analysis on specific body parts. MULAN is based on an improved Mask R-CNN framework with three head branches and a 3D feature fusion strategy. It achieves the state-of-the-art accuracy in the detection and tagging tasks on the DeepLesion dataset, which contains 32K lesions in the whole body. We also analyze the relationship between the three tasks and show that tag predictions can improve detection accuracy via a score refinement layer.