Deep Learning models for benign and malign Ocular Tumor Growth
Estimation
- URL: http://arxiv.org/abs/2107.04220v1
- Date: Fri, 9 Jul 2021 05:40:25 GMT
- Title: Deep Learning models for benign and malign Ocular Tumor Growth
Estimation
- Authors: Mayank Goswami
- Abstract summary: Clinicians often face issues in selecting suitable image processing algorithm for medical imaging data.
A strategy for the selection of a proper model is presented here.
- Score: 3.1558405181807574
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Relatively abundant availability of medical imaging data has provided
significant support in the development and testing of Neural Network based
image processing methods. Clinicians often face issues in selecting suitable
image processing algorithm for medical imaging data. A strategy for the
selection of a proper model is presented here. The training data set comprises
optical coherence tomography (OCT) and angiography (OCT-A) images of 50 mice
eyes with more than 100 days follow-up. The data contains images from treated
and untreated mouse eyes. Four deep learning variants are tested for automatic
(a) differentiation of tumor region with healthy retinal layer and (b)
segmentation of 3D ocular tumor volumes. Exhaustive sensitivity analysis of
deep learning models is performed with respect to the number of training and
testing images using 8 eight performance indices to study accuracy,
reliability/reproducibility, and speed. U-net with UVgg16 is best for malign
tumor data set with treatment (having considerable variation) and U-net with
Inception backbone for benign tumor data (with minor variation). Loss value and
root mean square error (R.M.S.E.) are found most and least sensitive
performance indices, respectively. The performance (via indices) is found to be
exponentially improving regarding a number of training images. The segmented
OCT-Angiography data shows that neovascularization drives the tumor volume.
Image analysis shows that photodynamic imaging-assisted tumor treatment
protocol is transforming an aggressively growing tumor into a cyst. An
empirical expression is obtained to help medical professionals to choose a
particular model given the number of images and types of characteristics. We
recommend that the presented exercise should be taken as standard practice
before employing a particular deep learning model for biomedical image
analysis.
Related papers
- Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images [45.29301790646322]
Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characterization.
We propose CADe, for segmenting lung nodules in a zero-shot manner using a variant of the Segment Anything Model called MedSAM.
We also propose, CADx, a method for the nodule characterization as benign/malignant by making a gallery of radiomic features and aligning image-feature pairs through contrastive learning.
arXiv Detail & Related papers (2024-07-02T19:30:25Z) - Integrating Preprocessing Methods and Convolutional Neural Networks for
Effective Tumor Detection in Medical Imaging [0.0]
This research presents a machine-learning approach for tumor detection in medical images using convolutional neural networks (CNNs)
The study focuses on preprocessing techniques to enhance image features relevant to tumor detection, followed by developing and training a CNN model for accurate classification.
Experimental results demonstrate the effectiveness of the proposed approach in accurately detecting tumors in medical images.
arXiv Detail & Related papers (2024-02-25T23:49:05Z) - A Two-Stage Generative Model with CycleGAN and Joint Diffusion for
MRI-based Brain Tumor Detection [41.454028276986946]
We propose a novel framework Two-Stage Generative Model (TSGM) to improve brain tumor detection and segmentation.
CycleGAN is trained on unpaired data to generate abnormal images from healthy images as data prior.
VE-JP is implemented to reconstruct healthy images using synthetic paired abnormal images as a guide.
arXiv Detail & Related papers (2023-11-06T12:58:26Z) - Automated ensemble method for pediatric brain tumor segmentation [0.0]
This study introduces a novel ensemble approach using ONet and modified versions of UNet.
Data augmentation ensures robustness and accuracy across different scanning protocols.
Results indicate that this advanced ensemble approach offers promising prospects for enhanced diagnostic accuracy.
arXiv Detail & Related papers (2023-08-14T15:29:32Z) - Classification of lung cancer subtypes on CT images with synthetic
pathological priors [41.75054301525535]
Cross-scale associations exist in the image patterns between the same case's CT images and its pathological images.
We propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on CT images.
arXiv Detail & Related papers (2023-08-09T02:04:05Z) - Brain Tumor Segmentation from MRI Images using Deep Learning Techniques [3.1498833540989413]
A public MRI dataset contains 3064 TI-weighted images from 233 patients with three variants of brain tumor, viz. meningioma, glioma, and pituitary tumor.
The dataset files were converted and preprocessed before indulging into the methodology which employs implementation and training of some well-known image segmentation deep learning models.
The experimental findings showed that among all the applied approaches, the recurrent residual U-Net which uses Adam reaches a Mean Intersection Over Union of 0.8665 and outperforms other compared state-of-the-art deep learning models.
arXiv Detail & Related papers (2023-04-29T13:33:21Z) - SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on
Medical Images [47.35184075381965]
We present a data augmentation method for generating synthetic medical images using cycle-consistency Generative Adversarial Networks (GANs)
The proposed GANs-based model can generate a tumor image from a normal image, and in turn, it can also generate a normal image from a tumor image.
We train the classification model using real images with classic data augmentation methods and classification models using synthetic images.
arXiv Detail & Related papers (2020-11-15T14:01:24Z) - Robust Pancreatic Ductal Adenocarcinoma Segmentation with
Multi-Institutional Multi-Phase Partially-Annotated CT Scans [25.889684822655255]
Pancreatic ductal adenocarcinoma (PDAC) segmentation is one of the most challenging tumor segmentation tasks.
Based on a new self-learning framework, we propose to train the PDAC segmentation model using a much larger quantity of patients.
Experiment results show that our proposed method provides an absolute improvement of 6.3% Dice score over the strong baseline of nnUNet trained on annotated images.
arXiv Detail & Related papers (2020-08-24T18:50:30Z) - Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo
Hyperspectral Tumor Type Classification [49.32653090178743]
We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning.
Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
arXiv Detail & Related papers (2020-07-02T12:00:53Z) - An interpretable classifier for high-resolution breast cancer screening
images utilizing weakly supervised localization [45.00998416720726]
We propose a framework to address the unique properties of medical images.
This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions.
It then applies another higher-capacity network to collect details from chosen regions.
Finally, it employs a fusion module that aggregates global and local information to make a final prediction.
arXiv Detail & Related papers (2020-02-13T15:28:42Z)
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.