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

arXiv: https://arxiv.org/abs/1908.04373 · HTML: https://mlsanity.com/paper/1908.04373/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2019.

## Built on by (most cited first, 30 of 39)

- [CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://mlsanity.com/paper/2301.00785/) (2023)
- [PACO: Parts and Attributes of Common Objects](https://mlsanity.com/paper/2301.01795/) (2023)
- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography](https://mlsanity.com/paper/2405.18356/) (2024)
- [The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography](https://mlsanity.com/paper/2406.05231/) (2024)
- [SATr: Slice Attention with Transformer for Universal Lesion Detection](https://mlsanity.com/paper/2203.07373/) (2022)
- [Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss](https://mlsanity.com/paper/2105.01218/) (2021)
- [CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans](https://mlsanity.com/paper/2301.12291/) (2023)
- [Asymmetric 3D Context Fusion for Universal Lesion Detection](https://mlsanity.com/paper/2109.08684/) (2021)
- [Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection](https://mlsanity.com/paper/2105.01828/) (2021)
- [Universal Lesion Detection in CT Scans using Neural Network Ensembles](https://mlsanity.com/paper/2111.04886/) (2021)
- [Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization](https://mlsanity.com/paper/2204.09334/) (2022)
- [Conditional Training with Bounding Map for Universal Lesion Detection](https://mlsanity.com/paper/2103.12277/) (2021)
- [RECIST-Net: Lesion detection via grouping keypoints on RECIST-based annotation](https://mlsanity.com/paper/2107.08715/) (2021)
- [View-Disentangled Transformer for Brain Lesion Detection](https://mlsanity.com/paper/2209.09657/) (2022)
- [Anatomy-Aware Lymph Node Detection in Chest CT using Implicit Station Stratification](https://mlsanity.com/paper/2307.15271/) (2023)
- [YOLO-MED : Multi-Task Interaction Network for Biomedical Images](https://mlsanity.com/paper/2403.00245/) (2024)
- [Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer](https://mlsanity.com/paper/2404.03819/) (2024)
- [RECIST Weakly Supervised Lesion Segmentation via Label-Space Co-Training](https://mlsanity.com/paper/2303.00205/) (2023)
- [Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data](https://mlsanity.com/paper/2103.12972/) (2021)
- [DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection](https://mlsanity.com/paper/2203.06886/) (2022)
- [Ultrasound Detection of Subquadricipital Recess Distension](https://mlsanity.com/paper/2211.12089/) (2022)
- [An End-to-End Framework For Universal Lesion Detection With Missing Annotations](https://mlsanity.com/paper/2303.15024/) (2023)
- [Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training](https://mlsanity.com/paper/2309.13306/) (2023)
- [Optimizing Universal Lesion Segmentation: State Space Model-Guided Hierarchical Networks with Feature Importance Adjustment](https://mlsanity.com/paper/2404.17235/) (2024)
- [Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT](https://mlsanity.com/paper/2504.05591/) (2025)
- [3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset](https://mlsanity.com/paper/2602.12820/) (2026)
- [An Efficient Anchor-free Universal Lesion Detection in CT-scans](https://mlsanity.com/paper/2203.16074/) (2022)
- [Integrating features from lymph node stations for metastatic lymph node detection](https://mlsanity.com/paper/2301.03202/) (2023)
- [Mixed-order self-paced curriculum learning for universal lesion detection](https://mlsanity.com/paper/2302.04677/) (2023)

## Abstract (arXiv)

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.