LegoNet: Alternating Model Blocks for Medical Image Segmentation

• URL: http://arxiv.org/abs/2306.03494v1
• Date: Tue, 6 Jun 2023 08:22:47 GMT
• Title: LegoNet: Alternating Model Blocks for Medical Image Segmentation
• Authors: Ikboljon Sobirov, Cheng Xie, Muhammad Siddique, Parijat Patel, Kenneth Chan, Thomas Halborg, Christos Kotanidis, Zarqiash Fatima, Henry West, Keith Channon, Stefan Neubauer, Charalambos Antoniades, and Mohammad Yaqub
• Abstract summary: We propose to alternate structurally different types of blocks to generate a new architecture, mimicking how Lego blocks can be assembled together. Using two CNN-based and one SwinViT-based blocks, we investigate three variations to the so-called LegoNet that applies the new concept of block alternation for the segmentation task in medical imaging.
• Score: 0.7550390281305251
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Since the emergence of convolutional neural networks (CNNs), and later vision transformers (ViTs), the common paradigm for model development has always been using a set of identical block types with varying parameters/hyper-parameters. To leverage the benefits of different architectural designs (e.g. CNNs and ViTs), we propose to alternate structurally different types of blocks to generate a new architecture, mimicking how Lego blocks can be assembled together. Using two CNN-based and one SwinViT-based blocks, we investigate three variations to the so-called LegoNet that applies the new concept of block alternation for the segmentation task in medical imaging. We also study a new clinical problem which has not been investigated before, namely the right internal mammary artery (RIMA) and perivascular space segmentation from computed tomography angiography (CTA) which has demonstrated a prognostic value to major cardiovascular outcomes. We compare the model performance against popular CNN and ViT architectures using two large datasets (e.g. achieving 0.749 dice similarity coefficient (DSC) on the larger dataset). We evaluate the performance of the model on three external testing cohorts as well, where an expert clinician made corrections to the model segmented results (DSC>0.90 for the three cohorts). To assess our proposed model for suitability in clinical use, we perform intra- and inter-observer variability analysis. Finally, we investigate a joint self-supervised learning approach to assess its impact on model performance. The code and the pretrained model weights will be available upon acceptance.

Related papers

• Architecture Analysis and Benchmarking of 3D U-shaped Deep Learning Models for Thoracic Anatomical Segmentation [0.8897689150430447]
  We conduct the first systematic benchmark study for variants of 3D U-shaped models. Our study examines the impact of different attention mechanisms, the number of resolution stages, and network configurations on segmentation accuracy and computational complexity.
  arXiv  Detail & Related papers  (2024-02-05T17:43:02Z)
• Unlocking the Heart Using Adaptive Locked Agnostic Networks [4.613517417540153]
  Supervised training of deep learning models for medical imaging applications requires a significant amount of labeled data. To address this limitation, we introduce the Adaptive Locked Agnostic Network (ALAN) ALAN involves self-supervised visual feature extraction using a large backbone model to produce robust semantic self-segmentation. Our findings demonstrate that the self-supervised backbone model robustly identifies anatomical subregions of the heart in an apical four-chamber view.
  arXiv  Detail & Related papers  (2023-09-21T09:06:36Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Prompt Tuning for Parameter-efficient Medical Image Segmentation [79.09285179181225]
  We propose and investigate several contributions to achieve a parameter-efficient but effective adaptation for semantic segmentation on two medical imaging datasets. We pre-train this architecture with a dedicated dense self-supervision scheme based on assignments to online generated prototypes. We demonstrate that the resulting neural network model is able to attenuate the gap between fully fine-tuned and parameter-efficiently adapted models.
  arXiv  Detail & Related papers  (2022-11-16T21:55:05Z)
• IterMiUnet: A lightweight architecture for automatic blood vessel segmentation [10.538564380139483]
  This paper proposes IterMiUnet, a new lightweight convolution-based segmentation model. It overcomes its heavily parametrized nature by incorporating the encoder-decoder structure of MiUnet model within it. The proposed model has a lot of potential to be utilized as a tool for the early diagnosis of many diseases.
  arXiv  Detail & Related papers  (2022-08-02T14:33:14Z)
• Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation [13.158995287578316]
  We propose a dynamic architecture network named Med-DANet to achieve effective accuracy and efficiency trade-off. For each slice of the input 3D MRI volume, our proposed method learns a slice-specific decision by the Decision Network. Our proposed method achieves comparable or better results than previous state-of-the-art methods for 3D MRI brain tumor segmentation.
  arXiv  Detail & Related papers  (2022-06-14T03:25:58Z)
• Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation [51.916106055115755]
  We propose a new CNN architecture that is pose and scale invariant thanks to the use of Spatial Transformer Network (STN) Our architecture is composed of three sequential modules that are estimated together during training. We test the proposed method in kidney and renal tumor segmentation on abdominal pediatric CT scanners.
  arXiv  Detail & Related papers  (2021-07-06T14:50:03Z)
• Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features [5.938730586521215]
  Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations. We propose a medical classifier that leverages semantic structural concepts learned via multi-resolution segmentation feature maps.
  arXiv  Detail & Related papers  (2020-10-31T00:16:53Z)
• DONet: Dual Objective Networks for Skin Lesion Segmentation [77.9806410198298]
  We propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation. Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives. To address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM)
  arXiv  Detail & Related papers  (2020-08-19T06:02:46Z)
• Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE [66.63629641650572]
  We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices. We also introduce a novel evaluation method for generated volumes that quantifies how well their segmentations match those of true brain anatomy.
  arXiv  Detail & Related papers  (2020-07-09T13:23:15Z)
• Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response [49.86828302591469]
  In this paper, we apply transfer learning to the prediction of anti-cancer drug response. We apply the classic transfer learning framework that trains a prediction model on the source dataset and refines it on the target dataset. The ensemble transfer learning pipeline is implemented using LightGBM and two deep neural network (DNN) models with different architectures.
  arXiv  Detail & Related papers  (2020-05-13T20:29:48Z)

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.