# Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster

arXiv: https://arxiv.org/abs/1907.03958 · HTML: https://mlsanity.com/paper/1907.03958/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Stabilized Medical Image Attacks](https://mlsanity.com/paper/2103.05232/) (2021)
- [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)
- [RECIST-Net: Lesion detection via grouping keypoints on RECIST-based annotation](https://mlsanity.com/paper/2107.08715/) (2021)
- [Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network](https://mlsanity.com/paper/2203.03640/) (2022)
- [Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance](https://mlsanity.com/paper/2210.14645/) (2022)

## Abstract (arXiv)

Accurate lesion detection in computer tomography (CT) slices benefits pathologic organ analysis in the medical diagnosis process. More recently, it has been tackled as an object detection problem using the Convolutional Neural Networks (CNNs). Despite the achievements from off-the-shelf CNN models, the current detection accuracy is limited by the inability of CNNs on lesions at vastly different scales. In this paper, we propose a Multi-Scale Booster (MSB) with channel and spatial attention integrated into the backbone Feature Pyramid Network (FPN). In each pyramid level, the proposed MSB captures fine-grained scale variations by using Hierarchically Dilated Convolutions (HDC). Meanwhile, the proposed channel and spatial attention modules increase the network's capability of selecting relevant features response for lesion detection. Extensive experiments on the DeepLesion benchmark dataset demonstrate that the proposed method performs superiorly against state-of-the-art approaches.