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Found 14 papers, 0 papers with code
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.
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 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.
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.
