# Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

arXiv: https://arxiv.org/abs/1906.02283 · HTML: https://mlsanity.com/paper/1906.02283/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 29 of 29)

- [nnDetection: A Self-configuring Method for Medical Object Detection](https://mlsanity.com/paper/2106.00817/) (2021)
- [SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation](https://mlsanity.com/paper/2106.01544/) (2021)
- [Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion](https://mlsanity.com/paper/2111.04911/) (2021)
- [SATr: Slice Attention with Transformer for Universal Lesion Detection](https://mlsanity.com/paper/2203.07373/) (2022)
- [Asymmetric 3D Context Fusion for Universal Lesion Detection](https://mlsanity.com/paper/2109.08684/) (2021)
- [SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching](https://mlsanity.com/paper/2104.05215/) (2021)
- [Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation](https://mlsanity.com/paper/2404.14852/) (2024)
- [Universal Lesion Detection in CT Scans using Neural Network Ensembles](https://mlsanity.com/paper/2111.04886/) (2021)
- [Enhancing Object Detection for Autonomous Driving by Optimizing Anchor Generation and Addressing Class Imbalance](https://mlsanity.com/paper/2104.03888/) (2021)
- [Conditional Training with Bounding Map for Universal Lesion Detection](https://mlsanity.com/paper/2103.12277/) (2021)
- [Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images](https://mlsanity.com/paper/2104.09699/) (2021)
- [RECIST-Net: Lesion detection via grouping keypoints on RECIST-based annotation](https://mlsanity.com/paper/2107.08715/) (2021)
- [MitoDet: Simple and robust mitosis detection](https://mlsanity.com/paper/2109.01485/) (2021)
- [Weakly-Supervised Detection of Bone Lesions in CT](https://mlsanity.com/paper/2402.00175/) (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)
- [Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images](https://mlsanity.com/paper/2112.01535/) (2021)
- [Colonoscopy polyp detection with massive endoscopic images](https://mlsanity.com/paper/2202.08730/) (2022)
- [DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection](https://mlsanity.com/paper/2203.06886/) (2022)
- [Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance](https://mlsanity.com/paper/2210.14645/) (2022)
- [Accurate Detection of Mediastinal Lesions with nnDetection](https://mlsanity.com/paper/2303.11214/) (2023)
- [Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training](https://mlsanity.com/paper/2309.13306/) (2023)
- [An Efficient Anchor-free Universal Lesion Detection in CT-scans](https://mlsanity.com/paper/2203.16074/) (2022)
- [Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors](https://mlsanity.com/paper/2208.04363/) (2022)
- [Integrating features from lymph node stations for metastatic lymph node detection](https://mlsanity.com/paper/2301.03202/) (2023)
- [3D Universal Lesion Detection and Tagging in CT with Self-Training](https://mlsanity.com/paper/2504.05201/) (2025)
- [Correcting Class Imbalances with Self-Training for Improved Universal Lesion Detection and Tagging](https://mlsanity.com/paper/2504.05207/) (2025)
- [PDSE: A Multiple Lesion Detector for CT Images using PANet and Deformable Squeeze-and-Excitation Block](https://mlsanity.com/paper/2506.03608/) (2025)
- [Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes](https://mlsanity.com/paper/2606.04365/) (2026)

## Abstract (arXiv)

Accurate, automated lesion detection in Computed Tomography (CT) is an important yet challenging task due to the large variation of lesion types, sizes, locations and appearances. Recent work on CT lesion detection employs two-stage region proposal based methods trained with centroid or bounding-box annotations. We propose a highly accurate and efficient one-stage lesion detector, by re-designing a RetinaNet to meet the particular challenges in medical imaging. Specifically, we optimize the anchor configurations using a differential evolution search algorithm. For training, we leverage the response evaluation criteria in solid tumors (RECIST) annotation which are measured in clinical routine. We incorporate dense masks from weak RECIST labels, obtained automatically using GrabCut, into the training objective, which in combination with other advancements yields new state-of-the-art performance. We evaluate our method on the public DeepLesion benchmark, consisting of 32,735 lesions across the body. Our one-stage detector achieves a sensitivity of 90.77% at 4 false positives per image, significantly outperforming the best reported methods by over 5%.