Performance Analysis of UNet and Variants for Medical Image Segmentation
- URL: http://arxiv.org/abs/2309.13013v1
- Date: Fri, 22 Sep 2023 17:20:40 GMT
- Title: Performance Analysis of UNet and Variants for Medical Image Segmentation
- Authors: Walid Ehab and Yongmin Li
- Abstract summary: This study aims to explore the application of deep learning models, particularly focusing on the UNet architecture and its variants, in medical image segmentation.
The findings reveal that the standard UNet, when extended with a deep network layer, is a proficient medical image segmentation model.
The Res-UNet and Attention Res-UNet architectures demonstrate smoother convergence and superior performance, particularly when handling fine image details.
- Score: 1.5410557873153836
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Medical imaging plays a crucial role in modern healthcare by providing
non-invasive visualisation of internal structures and abnormalities, enabling
early disease detection, accurate diagnosis, and treatment planning. This study
aims to explore the application of deep learning models, particularly focusing
on the UNet architecture and its variants, in medical image segmentation. We
seek to evaluate the performance of these models across various challenging
medical image segmentation tasks, addressing issues such as image
normalization, resizing, architecture choices, loss function design, and
hyperparameter tuning. The findings reveal that the standard UNet, when
extended with a deep network layer, is a proficient medical image segmentation
model, while the Res-UNet and Attention Res-UNet architectures demonstrate
smoother convergence and superior performance, particularly when handling fine
image details. The study also addresses the challenge of high class imbalance
through careful preprocessing and loss function definitions. We anticipate that
the results of this study will provide useful insights for researchers seeking
to apply these models to new medical imaging problems and offer guidance and
best practices for their implementation.
Related papers
- Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization [5.237321836999284]
We train and evaluate five published models on the publicly available FIVES fundus image dataset.
We find that image quality is a key factor determining segmentation outcomes.
arXiv Detail & Related papers (2024-06-21T09:12:34Z) - Understanding differences in applying DETR to natural and medical images [16.200340490559338]
Transformer-based detectors have shown success in computer vision tasks with natural images.
Medical imaging data presents unique challenges such as extremely large image sizes, fewer and smaller regions of interest, and object classes which can be differentiated only through subtle differences.
This study evaluates the applicability of these transformer-based design choices when applied to a screening mammography dataset.
arXiv Detail & Related papers (2024-05-27T22:06:42Z) - QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge [93.61262892578067]
Uncertainty in medical image segmentation tasks, especially inter-rater variability, presents a significant challenge.
This variability directly impacts the development and evaluation of automated segmentation algorithms.
We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ)
arXiv Detail & Related papers (2024-03-19T17:57:24Z) - Leveraging Foundation Models for Content-Based Medical Image Retrieval in Radiology [0.14631663747888957]
Content-based image retrieval has the potential to significantly improve diagnostic aid and medical research in radiology.
Current CBIR systems face limitations due to their specialization to certain pathologies, limiting their utility.
We propose using vision foundation models as powerful and versatile off-the-shelf feature extractors for content-based medical image retrieval.
arXiv Detail & Related papers (2024-03-11T10:06:45Z) - Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary
Task Integration [54.76511683427566]
This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information.
A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction.
The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
arXiv Detail & Related papers (2024-02-16T05:16:20Z) - On Sensitivity and Robustness of Normalization Schemes to Input
Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input.
DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases.
We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
arXiv Detail & Related papers (2023-06-23T03:09:03Z) - A Trustworthy Framework for Medical Image Analysis with Deep Learning [71.48204494889505]
TRUDLMIA is a trustworthy deep learning framework for medical image analysis.
It is anticipated that the framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises including COVID-19.
arXiv Detail & Related papers (2022-12-06T05:30:22Z) - Medical Image Segmentation on MRI Images with Missing Modalities: A
Review [3.9548535445908928]
The main goal of this research is to offer a performance evaluation of missing modality compensating networks.
Various approaches have been developed over time to mitigate this problem's negative implications.
arXiv Detail & Related papers (2022-03-11T19:33:26Z) - On the Robustness of Pretraining and Self-Supervision for a Deep
Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
We compare the impact of different training procedures for diabetic retinopathy grading.
We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions.
Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
arXiv Detail & Related papers (2021-06-25T08:32:45Z) - Cross Chest Graph for Disease Diagnosis with Structural Relational
Reasoning [2.7148274921314615]
Locating lesions is important in the computer-aided diagnosis of X-ray images.
General weakly-supervised methods have failed to consider the characteristics of X-ray images.
We propose the Cross-chest Graph (CCG), which improves the performance of automatic lesion detection.
arXiv Detail & Related papers (2021-01-22T08:24:04Z) - Few-shot Medical Image Segmentation using a Global Correlation Network
with Discriminative Embedding [60.89561661441736]
We propose a novel method for few-shot medical image segmentation.
We construct our few-shot image segmentor using a deep convolutional network trained episodically.
We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
arXiv Detail & Related papers (2020-12-10T04:01:07Z)
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.