# Volumetric Attention for 3D Medical Image Segmentation and Detection

arXiv: https://arxiv.org/abs/2004.01997 · HTML: https://mlsanity.com/paper/2004.01997/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2020.

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

- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Pyramid Medical Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2104.14702/) (2021)
- [Read Like a Radiologist: Efficient Vision-Language Model for 3D Medical Imaging Interpretation](https://mlsanity.com/paper/2412.13558/) (2024)
- [Asymmetric 3D Context Fusion for Universal Lesion Detection](https://mlsanity.com/paper/2109.08684/) (2021)
- [T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging](https://mlsanity.com/paper/2111.07535/) (2021)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Towards Simultaneous Segmentation of Liver Tumors and Intrahepatic Vessels via Cross-attention Mechanism](https://mlsanity.com/paper/2302.09785/) (2023)
- [PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images](https://mlsanity.com/paper/2103.00274/) (2021)
- [Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays](https://mlsanity.com/paper/2207.04394/) (2022)
- [CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2311.04942/) (2023)
- [Conditional Training with Bounding Map for Universal Lesion Detection](https://mlsanity.com/paper/2103.12277/) (2021)
- [Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions](https://mlsanity.com/paper/2209.10307/) (2022)
- [RECIST-Net: Lesion detection via grouping keypoints on RECIST-based annotation](https://mlsanity.com/paper/2107.08715/) (2021)
- [BezierSeg: Parametric Shape Representation for Fast Object Segmentation in Medical Images](https://mlsanity.com/paper/2108.00760/) (2021)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [Voxel-level Siamese Representation Learning for Abdominal Multi-Organ Segmentation](https://mlsanity.com/paper/2105.07672/) (2021)
- [Medical Image Segmentation Using Directional Window Attention](https://mlsanity.com/paper/2406.17471/) (2024)
- [3D Shuffle-Mixer: An Efficient Context-Aware Vision Learner of Transformer-MLP Paradigm for Dense Prediction in Medical Volume](https://mlsanity.com/paper/2204.06779/) (2022)
- [Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images](https://mlsanity.com/paper/2205.13199/) (2022)
- [Few-Shot 3D Volumetric Segmentation with Multi-Surrogate Fusion](https://mlsanity.com/paper/2408.14427/) (2024)
- [The Missing Piece: A Case for Pre-Training in 3D Medical Object Detection](https://mlsanity.com/paper/2509.15947/) (2025)
- [DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection](https://mlsanity.com/paper/2203.06886/) (2022)
- [BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision](https://mlsanity.com/paper/2310.07060/) (2023)
- [MOSAIC: A Multi-View 2.5D Organ Slice Selector with Cross-Attentional Reasoning for Anatomically-Aware CT Localization in Medical Organ Segmentation](https://mlsanity.com/paper/2505.10672/) (2025)
- [DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark](https://mlsanity.com/paper/2604.23187/) (2026)
- [MDA-Net: Multi-Dimensional Attention-Based Neural Network for 3D Image Segmentation](https://mlsanity.com/paper/2105.04508/) (2021)
- [A Survey on Causal Representation Learning and Future Work for Medical Image Analysis](https://mlsanity.com/paper/2210.16034/) (2022)
- [AI-CNet3D: An Anatomically-Informed Cross-Attention Network with Multi-Task Consistency Fine-tuning for 3D Glaucoma Classification](https://mlsanity.com/paper/2510.00882/) (2025)

## Abstract (arXiv)

A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction, and allows the use of pretrained 2D detection models when training data is limited, as is often the case for medical applications. Its integration in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver Tumor Segmentation (LiTS) Challenge, outperforming the previous challenge winner by 3.9 points and achieving top performance on the LiTS leader board at the time of paper submission. Detection experiments on the DeepLesion dataset also show that the addition of VA to existing object detectors enables a 69.1 sensitivity at 0.5 false positive per image, outperforming the best published results by 6.6 points.