# SATr: Slice Attention with Transformer for Universal Lesion Detection

arXiv: https://arxiv.org/abs/2203.07373 · HTML: https://mlsanity.com/paper/2203.07373/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 42 references that are in the map.
- First posted on arXiv 13 Mar 2022.

## Builds on (most cited first, 30 of 31)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias](https://mlsanity.com/paper/2106.03348/) (2021)
- [Eigen-CAM: Class Activation Map using Principal Components](https://mlsanity.com/paper/2008.00299/) (2020)
- [Conformer: Local Features Coupling Global Representations for Visual Recognition](https://mlsanity.com/paper/2105.03889/) (2021)
- [nnDetection: A Self-configuring Method for Medical Object Detection](https://mlsanity.com/paper/2106.00817/) (2021)
- [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)
- [Dual-stream Network for Visual Recognition](https://mlsanity.com/paper/2105.14734/) (2021)
- [Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels](https://mlsanity.com/paper/1906.02283/) (2019)
- [Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies](https://mlsanity.com/paper/2012.04872/) (2020)
- [3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection](https://mlsanity.com/paper/1806.09648/) (2018)
- [ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining](https://mlsanity.com/paper/1901.06359/) (2019)
- [MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection](https://mlsanity.com/paper/1909.04247/) (2019)
- [Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss](https://mlsanity.com/paper/2105.01218/) (2021)
- [Asymmetric 3D Context Fusion for Universal Lesion Detection](https://mlsanity.com/paper/2109.08684/) (2021)
- [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)
- [Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations](https://mlsanity.com/paper/2106.15918/) (2021)
- [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)
- [Conditional Training with Bounding Map for Universal Lesion Detection](https://mlsanity.com/paper/2103.12277/) (2021)
- [EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography](https://mlsanity.com/paper/2109.12474/) (2021)

## Built on by (most cited first, 17 of 17)

- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation](https://mlsanity.com/paper/2309.05674/) (2023)
- [CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2311.04942/) (2023)
- [Revisiting Computer-Aided Tuberculosis Diagnosis](https://mlsanity.com/paper/2307.02848/) (2023)
- [Towards All-in-One Medical Image Re-Identification](https://mlsanity.com/paper/2503.08173/) (2025)
- [DualAttNet: Synergistic Fusion of Image-level and Fine-Grained Disease Attention for Multi-Label Lesion Detection in Chest X-rays](https://mlsanity.com/paper/2306.13813/) (2023)
- [Enhancing medical vision-language contrastive learning via inter-matching relation modelling](https://mlsanity.com/paper/2401.10501/) (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)
- [Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training](https://mlsanity.com/paper/2309.13306/) (2023)
- [Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation](https://mlsanity.com/paper/2310.18656/) (2023)
- [Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT](https://mlsanity.com/paper/2504.05591/) (2025)
- [Mixed-order self-paced curriculum learning for universal lesion detection](https://mlsanity.com/paper/2302.04677/) (2023)
- [Interpretable 2D Vision Models for 3D Medical Images](https://mlsanity.com/paper/2307.06614/) (2023)
- [From Slices to Sequences: Autoregressive Tracking Transformer for Cohesive and Consistent 3D Lymph Node Detection in CT Scans](https://mlsanity.com/paper/2503.07933/) (2025)
- [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)
- [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 multi-slice-input detection approaches which model 3D context from multiple adjacent CT slices, but such methods still experience difficulty in obtaining a global representation among different slices and within each individual slice since they only use convolution-based fusion operations. In this paper, we propose a novel Slice Attention Transformer (SATr) block which can be easily plugged into convolution-based ULD backbones to form hybrid network structures. Such newly formed hybrid backbones can better model long-distance feature dependency via the cascaded self-attention modules in the Transformer block while still holding a strong power of modeling local features with the convolutional operations in the original backbone. Experiments with five state-of-the-art methods show that the proposed SATr block can provide an almost free boost to lesion detection accuracy without extra hyperparameters or special network designs.