Search Results for author:
Found 10 papers, 8 papers with code
Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides
In this study, we aimed to develop an AI model for segmentation of epithelial cells in sections from breast cancer.
Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting
For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance (MR) scans.
Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks
The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection.
RESECT-SEG: Open access annotations of intra-operative brain tumor ultrasound images
The proposed dataset contains tumor tissues and resection cavity annotations of the iUS images.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Hybrid guiding: A multi-resolution refinement approach for semantic segmentation of gigapixel histopathological images
To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumour segmentation.
Code-free development and deployment of deep segmentation models for digital pathology
Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the ability to write computer code or purchase commercial solutions.
Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms
A larger number of cases with meningiomas below 3ml might also be needed to improve the performance for the smallest tumors.
FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology
It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results.
Fast meningioma segmentation in T1-weighted MRI volumes using a lightweight 3D deep learning architecture
We studied two different 3D neural network architectures: (i) a simple encoder-decoder similar to a 3D U-Net, and (ii) a lightweight multi-scale architecture (PLS-Net).
