Deep neuroevolution for limited, heterogeneous data: proof-of-concept
application to Neuroblastoma brain metastasis using a small virtual pooled
image collection
- URL: http://arxiv.org/abs/2211.14499v1
- Date: Sat, 26 Nov 2022 07:03:37 GMT
- Title: Deep neuroevolution for limited, heterogeneous data: proof-of-concept
application to Neuroblastoma brain metastasis using a small virtual pooled
image collection
- Authors: Subhanik Purkayastha, Hrithwik Shalu, David Gutman, Shakeel Modak,
Ellen Basu, Brian Kushner, Kim Kramer, Sofia Haque and Joseph Stember
- Abstract summary: We seek to address both overfitting and generalizability by applying DNE to a virtually pooled data set consisting of images from various institutions.
Our use case is classifying neuroblastoma brain metastases on MRI. Neuroblastoma is well-suited for our goals because it is a rare cancer.
As in prior DNE work, we used a small training set, consisting of 30 normal and 30 metastasis-containing post-contrast MRI brain scans, with 37% outside images.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Artificial intelligence (AI) in radiology has made great strides in recent
years, but many hurdles remain. Overfitting and lack of generalizability
represent important ongoing challenges hindering accurate and dependable
clinical deployment. If AI algorithms can avoid overfitting and achieve true
generalizability, they can go from the research realm to the forefront of
clinical work. Recently, small data AI approaches such as deep neuroevolution
(DNE) have avoided overfitting small training sets. We seek to address both
overfitting and generalizability by applying DNE to a virtually pooled data set
consisting of images from various institutions. Our use case is classifying
neuroblastoma brain metastases on MRI. Neuroblastoma is well-suited for our
goals because it is a rare cancer. Hence, studying this pediatric disease
requires a small data approach. As a tertiary care center, the neuroblastoma
images in our local Picture Archiving and Communication System (PACS) are
largely from outside institutions. These multi-institutional images provide a
heterogeneous data set that can simulate real world clinical deployment. As in
prior DNE work, we used a small training set, consisting of 30 normal and 30
metastasis-containing post-contrast MRI brain scans, with 37% outside images.
The testing set was enriched with 83% outside images. DNE converged to a
testing set accuracy of 97%. Hence, the algorithm was able to predict image
class with near-perfect accuracy on a testing set that simulates real-world
data. Hence, the work described here represents a considerable contribution
toward clinically feasible AI.
Related papers
- Classification of Prostate Cancer in 3D Magnetic Resonance Imaging Data based on Convolutional Neural Networks [0.0]
Prostate cancer is a commonly diagnosed cancerous disease among men world-wide.
CNN are evaluated on their abilities to reliably classify whether an MRI sequence contains malignant lesions.
The best result was achieved by a ResNet3D, yielding an average precision score of 0.4583 and AUC ROC score of 0.6214.
arXiv Detail & Related papers (2024-04-16T13:18:02Z) - Uncertainty Quantification in Detecting Choroidal Metastases on MRI via Evolutionary Strategies [0.0]
Uncertainty quantification plays a vital role in facilitating the practical implementation of AI in radiology.
We employed DNE to train a simple Convolutional Neural Network (CNN) with MRI images of the eyes for binary classification.
We found that subjective features appreciated by human radiologists explained images for which uncertainty was high.
arXiv Detail & Related papers (2024-04-12T23:49:37Z) - UniBrain: Universal Brain MRI Diagnosis with Hierarchical
Knowledge-enhanced Pre-training [66.16134293168535]
We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain.
Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
arXiv Detail & Related papers (2023-09-13T09:22:49Z) - Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks.
We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
arXiv Detail & Related papers (2023-05-25T16:29:16Z) - Deep neuroevolution to predict primary brain tumor grade from functional
MRI adjacency matrices [0.0]
We show that AI can learn to discern between two important brain tumor types, high-grade glioma (HGG) and low-grade glioma (LGG)
We trained a convolutional neural networks (CNN) with the method of deep neuroevolution (DNE)
After training on just 30 adjacency matrices, our CNN could tell HGG apart from LGG with perfect testing set accuracy.
arXiv Detail & Related papers (2022-11-26T07:13:31Z) - Direct evaluation of progression or regression of disease burden in
brain metastatic disease with Deep Neuroevolution [0.0]
Deep neuroevolution (DNE) can produce radiology artificial intelligence (AI) that performs well on small training sets.
Here we use DNE for function approximation that predicts progression versus regression of metastatic brain disease.
arXiv Detail & Related papers (2022-03-24T05:29:09Z) - Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI [5.463018151091638]
We show that state-of-the-art deep learning pipeline nnU-Net has difficulties to generalize to unseen abnormal cases.
We propose to train a deep neural network to minimize a percentile of the distribution of per-volume loss over the dataset.
We validated our approach using a dataset of 368 fetal brain T2w MRIs.
arXiv Detail & Related papers (2021-08-09T17:00:21Z) - In-Line Image Transformations for Imbalanced, Multiclass Computer Vision
Classification of Lung Chest X-Rays [91.3755431537592]
This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data.
Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states.
This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
arXiv Detail & Related papers (2021-04-06T02:01:43Z) - Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
arXiv Detail & Related papers (2020-10-14T16:50:50Z) - Domain Generalization for Medical Imaging Classification with
Linear-Dependency Regularization [59.5104563755095]
We introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification.
Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding.
arXiv Detail & Related papers (2020-09-27T12:30:30Z) - Interpretation of 3D CNNs for Brain MRI Data Classification [56.895060189929055]
We extend the previous findings in gender differences from diffusion-tensor imaging on T1 brain MRI scans.
We provide the voxel-wise 3D CNN interpretation comparing the results of three interpretation methods.
arXiv Detail & Related papers (2020-06-20T17:56:46Z)
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.