AttentNet: Fully Convolutional 3D Attention for Lung Nodule Detection

• URL: http://arxiv.org/abs/2407.14464v1
• Date: Fri, 19 Jul 2024 17:06:27 GMT
• Title: AttentNet: Fully Convolutional 3D Attention for Lung Nodule Detection
• Authors: Majedaldein Almahasneh, Xianghua Xie, Adeline Paiement,
• Abstract summary: In 3D medical imaging, such as 3D pulmonary CT scans, efficient processing is crucial due to the large data volume. We investigate the possibility of incorporating fully convolutional (conv.) attention in 3D context. We present two 3D fully conv. attention blocks, demonstrating their effectiveness in 3D context.
• Score: 4.47196217712431
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motivated by the increasing popularity of attention mechanisms, we observe that popular convolutional (conv.) attention models like Squeeze-and-Excite (SE) and Convolutional Block Attention Module (CBAM) rely on expensive multi-layer perception (MLP) layers. These MLP layers significantly increase computational complexity, making such models less applicable to 3D image contexts, where data dimensionality and computational costs are higher. In 3D medical imaging, such as 3D pulmonary CT scans, efficient processing is crucial due to the large data volume. Traditional 2D attention generalized to 3D increases the computational load, creating demand for more efficient attention mechanisms for 3D tasks. We investigate the possibility of incorporating fully convolutional (conv.) attention in 3D context. We present two 3D fully conv. attention blocks, demonstrating their effectiveness in 3D context. Using pulmonary CT scans for 3D lung nodule detection, we present AttentNet, an automated lung nodule detection framework from CT images, performing detection as an ensemble of two stages, candidate proposal and false positive (FP) reduction. We compare the proposed 3D attention blocks to popular 2D conv. attention methods generalized to 3D modules and to self-attention units. For the FP reduction stage, we also use a joint analysis approach to aggregate spatial information from different contextual levels. We use LUNA-16 lung nodule detection dataset to demonstrate the benefits of the proposed fully conv. attention blocks compared to baseline popular lung nodule detection methods when no attention is used. Our work does not aim at achieving state-of-the-art results in the lung nodule detection task, rather to demonstrate the benefits of incorporating fully conv. attention within a 3D context.

Related papers

• CatFree3D: Category-agnostic 3D Object Detection with Diffusion [63.75470913278591]
  We introduce a novel pipeline that decouples 3D detection from 2D detection and depth prediction. We also introduce the Normalised Hungarian Distance (NHD) metric for an accurate evaluation of 3D detection results.
  arXiv  Detail & Related papers  (2024-08-22T22:05:57Z)
• 3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features [70.50665869806188]
  3DiffTection is a state-of-the-art method for 3D object detection from single images. We fine-tune a diffusion model to perform novel view synthesis conditioned on a single image. We further train the model on target data with detection supervision.
  arXiv  Detail & Related papers  (2023-11-07T23:46:41Z)
• Homography Loss for Monocular 3D Object Detection [54.04870007473932]
  A differentiable loss function, termed as Homography Loss, is proposed to achieve the goal, which exploits both 2D and 3D information. Our method yields the best performance compared with the other state-of-the-arts by a large margin on KITTI 3D datasets.
  arXiv  Detail & Related papers  (2022-04-02T03:48:03Z)
• FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection [81.79171905308827]
  We propose frustum-aware geometric reasoning (FGR) to detect vehicles in point clouds without any 3D annotations. Our method consists of two stages: coarse 3D segmentation and 3D bounding box estimation. It is able to accurately detect objects in 3D space with only 2D bounding boxes and sparse point clouds.
  arXiv  Detail & Related papers  (2021-05-17T07:29:55Z)
• R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation [17.343802171952195]
  We propose a novel model, namely, Recurrent Residual 3D U-Net (R2U3D), for the 3D lung segmentation task. In particular, the proposed model integrates 3D convolution into the Recurrent Residual Neural Network based on U-Net. The proposed R2U3D network is trained on the publicly available dataset LUNA16 and it achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-05T19:17:14Z)
• Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans [72.04652116817238]
  We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification. We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
  arXiv  Detail & Related papers  (2021-01-14T03:45:01Z)
• 3D Axial-Attention for Lung Nodule Classification [0.11458853556386794]
  We propose to use 3D Axial-Attention, which requires a fraction of the computing power of a regular Non-Local network. We solve the position invariant problem of the Non-Local network by proposing adding 3D positional encoding to shared embeddings. Our results show that the 3D Axial-Attention model achieves state-of-the-art performance on all evaluation metrics including AUC and Accuracy.
  arXiv  Detail & Related papers  (2020-12-28T06:49:09Z)
• Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation [18.76436457395804]
  Multi-organ segmentation is one of most successful applications of deep learning in medical image analysis. Deep convolutional neural nets (CNNs) have shown great promise in achieving clinically applicable image segmentation performance on CT or MRI images. We propose a new framework for combining 3D and 2D models, in which the segmentation is realized through high-resolution 2D convolutions.
  arXiv  Detail & Related papers  (2020-12-16T21:39:53Z)
• Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices [48.85784310158493]
  We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset. The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
  arXiv  Detail & Related papers  (2020-12-16T07:11:16Z)
• VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data [13.805816310795256]
  We present an end-to-end deep learning method, VC-Net, for robust extraction of 3D microvasculature. The core novelty is to automatically leverage the volume visualization technique (MIP) to enhance the 3D data exploration. A multi-stream convolutional neural network is proposed to learn the 3D volume and 2D MIP features respectively and then explore their inter-dependencies in a joint volume-composition embedding space.
  arXiv  Detail & Related papers  (2020-09-14T04:15:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.