# Conditional Training with Bounding Map for Universal Lesion Detection

arXiv: https://arxiv.org/abs/2103.12277 · HTML: https://mlsanity.com/paper/2103.12277/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 33 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 27 of 27)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Imbalance Problems in Object Detection: A Review](https://mlsanity.com/paper/1909.00169/) (2019)
- [Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network](https://mlsanity.com/paper/1711.08324/) (2017)
- [Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database](https://mlsanity.com/paper/1711.10535/) (2017)
- [Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT](https://mlsanity.com/paper/2009.02577/) (2020)
- [MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation](https://mlsanity.com/paper/1908.04373/) (2019)
- [Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels](https://mlsanity.com/paper/1906.02283/) (2019)
- [Volumetric Attention for 3D Medical Image Segmentation and Detection](https://mlsanity.com/paper/2004.01997/) (2020)
- [3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection](https://mlsanity.com/paper/1806.09648/) (2018)
- [NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentation](https://mlsanity.com/paper/1907.11320/) (2019)
- [ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining](https://mlsanity.com/paper/1901.06359/) (2019)
- [Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection](https://mlsanity.com/paper/2007.05225/) (2020)
- [MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection](https://mlsanity.com/paper/1909.04247/) (2019)
- [AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes](https://mlsanity.com/paper/2005.01969/) (2020)
- [Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention](https://mlsanity.com/paper/1907.04052/) (2019)
- [Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression](https://mlsanity.com/paper/2008.13254/) (2020)
- [Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices](https://mlsanity.com/paper/2012.08770/) (2020)
- [Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster](https://mlsanity.com/paper/1907.03958/) (2019)
- [Bounding Maps for Universal Lesion Detection](https://mlsanity.com/paper/2007.09383/) (2020)
- [3D Anchor-Free Lesion Detector on Computed Tomography Scans](https://mlsanity.com/paper/1908.11324/) (2019)
- [3DFPN-HS2: 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection](https://mlsanity.com/paper/1906.03467/) (2019)
- [3D Aggregated Faster R-CNN for General Lesion Detection](https://mlsanity.com/paper/2001.11071/) (2020)
- [Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network](https://mlsanity.com/paper/2007.16133/) (2020)

## Built on by (most cited first, 5 of 5)

- [SATr: Slice Attention with Transformer for Universal Lesion Detection](https://mlsanity.com/paper/2203.07373/) (2022)
- [Which images to label for few-shot medical landmark detection?](https://mlsanity.com/paper/2112.04386/) (2021)
- [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)
- [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)

## Abstract (arXiv)

Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by coarse-to-fine two-stage detection approaches, but such two-stage ULD methods still suffer from issues like imbalance of positive v.s. negative anchors during object proposal and insufficient supervision problem during localization regression and classification of the region of interest (RoI) proposals. While leveraging pseudo segmentation masks such as bounding map (BM) can reduce the above issues to some degree, it is still an open problem to effectively handle the diverse lesion shapes and sizes in ULD. In this paper, we propose a BM-based conditional training for two-stage ULD, which can (i) reduce positive vs. negative anchor imbalance via BM-based conditioning (BMC) mechanism for anchor sampling instead of traditional IoU-based rule; and (ii) adaptively compute size-adaptive BM (ABM) from lesion bounding box, which is used for improving lesion localization accuracy via ABMsupervised segmentation. Experiments with four state-of-the-art methods show that the proposed approach can bring an almost free detection accuracy improvement without requiring expensive lesion mask annotations.