Search Results for author:
Found 8 papers, 5 papers with code
Rule-based outlier detection of AI-generated anatomy segmentations
There is a dire need for medical imaging datasets with accompanying annotations to perform downstream patient analysis.
Automatic classification of prostate MR series type using image content and metadata
With the wealth of medical image data, efficient curation is essential.
Towards Automatic Abdominal MRI Organ Segmentation: Leveraging Synthesized Data Generated From CT Labels
Our approach shows comparable results compared to fully-supervised segmentation methods trained on MR data.
Real-Time Dynamic Data Driven Deformable Registration for Image-Guided Neurosurgery: Computational Aspects
Current neurosurgical procedures utilize medical images of various modalities to enable the precise location of tumors and critical brain structures to plan accurate brain tumor resection.
Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations
As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections of computed tomography images of the chest, NSCLC-Radiomics, and the National Lung Screening Trial.
The NCI Imaging Data Commons as a platform for reproducible research in computational pathology
Conclusions: We conclude that the IDC facilitates approaching the reproducibility limit of CompPath research (i) by enabling researchers to reuse exactly the same datasets and (ii) by integrating with cloud ML services so that experiments can be run in identically configured computing environments.
Repeatability of Multiparametric Prostate MRI Radiomics Features
In this study we assessed the repeatability of the values of radiomics features for small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI) images.
Large scale digital prostate pathology image analysis combining feature extraction and deep neural network
In this work, we present an analysis pipeline that includes localization of the cancer region, grading, area ratio of different Gleason grades, and cytological/architectural feature extraction.
