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Found 45 papers, 2 papers with code
Edge-Enhanced Dilated Residual Attention Network for Multimodal Medical Image Fusion
Multimodal medical image fusion is a crucial task that combines complementary information from different imaging modalities into a unified representation, thereby enhancing diagnostic accuracy and treatment planning.
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis
Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow has been limited.
Opportunities for Persian Digital Humanities Research with Artificial Intelligence Language Models; Case Study: Forough Farrokhzad
This study explores the integration of advanced Natural Language Processing (NLP) and Artificial Intelligence (AI) techniques to analyze and interpret Persian literature, focusing on the poetry of Forough Farrokhzad.
Improving Pediatric Low-Grade Neuroepithelial Tumors Molecular Subtype Identification Using a Novel AUROC Loss Function for Convolutional Neural Networks
The baseline model was trained using binary cross entropy (BCE), and achieved an AUROC of 86. 11% for differentiating BRAF fusion and BRAF V600E mutations, which was improved to 87. 71% using our proposed AUROC loss function (p-value 0. 045).
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks
Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes.
Using Large Text-to-Image Models with Structured Prompts for Skin Disease Identification: A Case Study
This paper investigates the potential usage of large text-to-image (LTI) models for the automated diagnosis of a few skin conditions with rarity or a serious lack of annotated datasets.
Non-invasive Liver Fibrosis Screening on CT Images using Radiomics
The combination of hyperparameters and features that yielded the highest AUC was a logistic regression model with inputs features of maximum, energy, kurtosis, skewness, and small area high gray level emphasis extracted from non-contrast enhanced NC CT normalized using Gamma correction with $\gamma$ = 1. 5 (AUC, 0. 7833; 95% CI: 0. 7821, 0. 7845), (sensitivity, 0. 9091; 95% CI: 0. 9091, 0. 9091).
Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation
This paper proposes to address the Cobb angle measurement task using YOLACT, an instance segmentation model.
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images
Segmentation of regions of interest (ROIs) for identifying abnormalities is a leading problem in medical imaging.
Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models
In this work, we propose a weakly supervised approach to obtain regions of interest using binary class labels.
Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location
Conclusion: We achieved statistically significant improvements by incorporating tumor location into the CNN models.
Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input
Many high-performance classification models utilize complex CNN-based architectures for Alzheimer's Disease classification.
Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images
Training machine learning models to segment tumors and other anomalies in medical images is an important step for developing diagnostic tools but generally requires manually annotated ground truth segmentations, which necessitates significant time and resources.
Minimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning
Generalizability is the ultimate goal of Machine Learning (ML) image classifiers, for which noise and limited dataset size are among the major concerns.
Using Multi-modal Data for Improving Generalizability and Explainability of Disease Classification in Radiology
Traditional datasets for the radiological diagnosis tend to only provide the radiology image alongside the radiology report.
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines
Using PyRadiomics library for LGG vs. HGG classification, 288 radiomics datasets are formed; the combinations of 4 MRI sequences, 3 binWidths, 6 image normalization methods, and 4 tumor subregions.
Improving Disease Classification Performance and Explainability of Deep Learning Models in Radiology with Heatmap Generators
The paper (A. Karargyris and Moradi, 2021) that introduced this dataset developed a U-Net model, which was treated as the baseline model for this research, to show how the eye-gaze data can be used in multi-modal training for explainability improvement.
Exploring COVID-19 Related Stressors Using Topic Modeling
This study aims to apply natural language processing (NLP) on social media data to identify the psychosocial stressors during COVID-19 pandemic, and to analyze the trend on prevalence of stressors at different stages of the pandemic.
Localized Perturbations For Weakly-Supervised Segmentation of Glioma Brain Tumours
Deep convolutional neural networks (CNNs) have become an essential tool in the medical imaging-based computer-aided diagnostic pipeline.
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning
Brain tumor segmentation is a critical task for tumor volumetric analyses and AI algorithms.
Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification
By training a weak GAN and using its generated output image parallel to the regular GAN, the Vanishing Twin training improves semi-supervised image classification where image similarity can hurt classification tasks.
Multi-class Generative Adversarial Nets for Semi-supervised Image Classification
We propose a modification to the traditional training of GANs that allows for improved multi-class classification in similar classes of images in a semi-supervised learning framework.
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification
The model achieved AUC of 82% compared with AUC of 78. 48% for the baseline, which reassures the robustness and stability of our proposed transfer learning augmented with active learning framework while significantly reducing the size of training data.
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray
Gathering labeled data is a cumbersome task and requires time and resources which could further strain health care systems and radiologists at the early stages of a pandemic such as COVID-19.
Evaluating Knowledge Transfer in Neural Network for Medical Images
To address this issue, we propose a teacher-student learning framework to transfer knowledge from a carefully pre-trained convolutional neural network (CNN) teacher to a student CNN.
A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning
Multi-task learning (MTL) optimizes several learning tasks simultaneously and leverages their shared information to improve generalization and the prediction of the model for each task.
A Modified AUC for Training Convolutional Neural Networks: Taking Confidence into Account
Receiver operating characteristic (ROC) curve is an informative tool in binary classification and Area Under ROC Curve (AUC) is a popular metric for reporting performance of binary classifiers.
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images
Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively.
A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-weighted MRI using Convolutional Neural Networks
Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution.
Evolution-based Fine-tuning of CNNs for Prostate Cancer Detection
Convolutional Neural Networks (CNNs) have been used for automated detection of prostate cancer where Area Under Receiver Operating Characteristic (ROC) curve (AUC) is usually used as the performance metric.
A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI
Using a fine-tuning data of 115 patients from the target domain, dice score coefficient of 0. 85 and 0. 84 are achieved for segmentation of whole gland and transition zone, respectively, in the target domain.
Improving Prognostic Performance in Resectable Pancreatic Ductal Adenocarcinoma using Radiomics and Deep Learning Features Fusion in CT Images
It was shown that the proposed feature fusion method significantly improves the prognosis performance for overall survival in resectable PDAC cohorts, elevating the area under ROC curve by 51% compared to predefined radiomics features alone, by 16% compared to deep learning features alone, and by 32% compared to existing feature fusion and reduction methods for a combination of deep learning and predefined radiomics features.
CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging
The proposed CNN-based survival model outperformed the traditional CPH-based radiomics approach in terms of concordance index by 22%, providing a better fit for patients' survival patterns.
Prostate Cancer Detection using Deep Convolutional Neural Networks
As an integrated part of computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance imaging (DWI) has been intensively studied for accurate detection of prostate cancer.
Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma
The proposed deep transfer learning model for prognostication of PDAC achieved the area under the receiver operating characteristic curve of 0. 74, which was significantly higher than that of the traditional radiomics model (0. 56) as well as a CNN model trained from scratch (0. 50).
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks
Generative Adversarial Networks (GANs) have shown considerable promise for mitigating the challenge of data scarcity when building machine learning-driven analysis algorithms.
Discovery Radiomics via Evolutionary Deep Radiomic Sequencer Discovery for Pathologically-Proven Lung Cancer Detection
We evaluated the evolved deep radiomic sequencer (EDRS) discovered via the proposed evolutionary deep radiomic sequencer discovery framework against state-of-the-art radiomics-driven and discovery radiomics methods using clinical lung CT data with pathologically-proven diagnostic data from the LIDC-IDRI dataset.
Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields
Reducing MRI acquisition time can reduce patient discomfort and as a result reduces motion artifacts from the acquisition process.
Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model
Retrospective bias correction approaches are introduced as a more efficient way of bias correction compared to the prospective methods such that they correct for both of the scanner and anatomy-related bias fields in MR imaging.
Discovery Radiomics via StochasticNet Sequencers for Cancer Detection
In this study, we introduce a novel discovery radiomics framework where we directly discover custom radiomic features from the wealth of available medical imaging data.
Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data
Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literature where an entire image is retrieved or classified.
Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection
In this study, we propose a novel \textit{discovery radiomics} framework for generating custom radiomic sequences tailored for prostate cancer detection.
Discovery Radiomics for Pathologically-Proven Computed Tomography Lung Cancer Prediction
In this study, we take the idea of radiomics one step further by introducing the concept of discovery radiomics for lung cancer prediction using CT imaging data.
Autoencoding the Retrieval Relevance of Medical Images
Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data.
Evolving Fuzzy Image Segmentation with Self-Configuration
However, EFIS suffers from a few limitations when used in practice mainly due to some fixed parameters.
