# Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression

arXiv: https://arxiv.org/abs/2008.13254 · HTML: https://mlsanity.com/paper/2008.13254/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2020.

## Built on by (most cited first, 10 of 10)

- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [SATr: Slice Attention with Transformer for Universal Lesion Detection](https://mlsanity.com/paper/2203.07373/) (2022)
- [Transformer Lesion Tracker](https://mlsanity.com/paper/2206.06252/) (2022)
- [Conditional Training with Bounding Map for Universal Lesion Detection](https://mlsanity.com/paper/2103.12277/) (2021)
- [SAM++: Enhancing Anatomic Matching using Semantic Information and Structural Inference](https://mlsanity.com/paper/2306.13988/) (2023)
- [Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training](https://mlsanity.com/paper/2201.01426/) (2022)
- [A Flexible Three-Dimensional Hetero-phase Computed Tomography Hepatocellular Carcinoma (HCC) Detection Algorithm for Generalizable and Practical HCC Screening](https://mlsanity.com/paper/2108.07492/) (2021)
- [Mixed-order self-paced curriculum learning for universal lesion detection](https://mlsanity.com/paper/2302.04677/) (2023)
- [MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images](https://mlsanity.com/paper/2505.18741/) (2025)
- [RadYOLO: Computationally Efficient 3D Object Detection and Segmentation in CT and MRI](https://mlsanity.com/paper/2608.00508/) (2026)

## Abstract (arXiv)

Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians. Computer-aided lesion/significant-findings detection techniques are at the core of medical imaging, which remain very challenging due to the tremendously large variability of lesion appearance, location and size distributions in 3D imaging. In this work, we propose a novel deep anchor-free one-stage VULD framework that incorporates (1) P3DC operators to recycle the architectural configurations and pre-trained weights from the off-the-shelf 2D networks, especially ones with large capacities to cope with data variance, and (2) a new SPR method to effectively regress the 3D lesion spatial extents by pinpointing their representative key points on lesion surfaces. Experimental validations are first conducted on the public large-scale NIH DeepLesion dataset where our proposed method delivers new state-of-the-art quantitative performance. We also test VULD on our in-house dataset for liver tumor detection. VULD generalizes well in both large-scale and small-sized tumor datasets in CT imaging.