Search Results for author:
Found 11 papers, 11 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.
Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation
Conclusion: Using simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph.
Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation
Purpose: Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel.
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.
Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding
For the 1178 lymph nodes with a short-axis diameter $\geq10$ mm, our best performing approach reached a patient-wise recall of 92%, a false positive per patient ratio of 5, and a segmentation overlap of 80. 5%.
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).
