Search Results for author:
Found 15 papers, 7 papers with code
VLST: Virtual Lung Screening Trial for Lung Cancer Detection Using Virtual Imaging Trial
Objectives: To establish a virtual imaging trial (VIT) platform that accurately simulates real-world lung screening trials (LSTs) to assess the diagnostic accuracy of CT and CXR modalities.
What limits performance of weakly supervised deep learning for chest CT classification?
First, we examined model tolerance for noisy data by incrementally increasing error in the labels within the training data.
Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation
In this study, we leverage recent advancements in geometric deep learning and denoising diffusion probabilistic models to refine the segmentation results of the large intestine.
Virtual imaging trials improved the transparency and reliability of AI systems in COVID-19 imaging
In this study, COVID-19 serves as a case example to unveil the intrinsic and extrinsic factors influencing AI performance.
Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography
However, performance dropped to an AUC of 0. 65 and 0. 69 when evaluated on clinical and our simulated CVIT-COVID dataset.
Quality or Quantity: Toward a Unified Approach for Multi-organ Segmentation in Body CT
This demonstrates that quality data is the key to improving the model's performance.
Co-occurring Diseases Heavily Influence the Performance of Weakly Supervised Learning Models for Classification of Chest CT
Despite the potential of weakly supervised learning to automatically annotate massive amounts of data, little is known about its limitations for use in computer-aided diagnosis (CAD).
Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning
Compared to other methods, our model detects clinical features (mass margins) with equal or higher accuracy, provides a more detailed explanation of its prediction, and is better able to differentiate the classification-relevant parts of the image.
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography
Mammography poses important challenges that are not present in other computer vision tasks: datasets are small, confounding information is present, and it can be difficult even for a radiologist to decide between watchful waiting and biopsy based on a mammogram alone.
Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning
Pre-trained models outperformed random initialization across all diseases.
Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model
While breast cancer screening has been one of the most studied medical imaging applications of artificial intelligence, the development and evaluation of the algorithms are hindered due to the lack of well-annotated large-scale publicly available datasets.
Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features
Second, segmentation and classification models are connected with two different feature aggregation strategies to enhance the classification performance.
iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry
Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins (DT) using patient medical images.
Classification of Multiple Diseases on Body CT Scans using Weakly Supervised Deep Learning
Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports. Materials & Methods: This retrospective study included a total of 12, 092 patients (mean age 57 +- 18; 6, 172 women) for model development and testing (from 2012-2017).
Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes
This model reached a classification performance of AUROC greater than 0. 90 for 18 abnormalities, with an average AUROC of 0. 773 for all 83 abnormalities, demonstrating the feasibility of learning from unfiltered whole volume CT data.
