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FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology
Paper • 2011.06033 • Published • 1 -
Code-free development and deployment of deep segmentation models for digital pathology
Paper • 2111.08430 • Published -
Hybrid guiding: A multi-resolution refinement approach for semantic segmentation of gigapixel histopathological images
Paper • 2112.03455 • Published
André Pedersen
andreped
AI & ML interests
My research include machine learning, image analysis, software development, and computational statistics, mainly in the field of medicine. The data types range from 3D MRI images, gigapixel histopathological images, bronchoscopy videos, free text, and recently mobile sensor data.
Organizations
Collections
2
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Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting
Paper • 2305.14351 • Published • 1 -
Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks
Paper • 2304.08881 • Published -
Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation
Paper • 2112.11541 • Published -
Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding
Paper • 2102.06515 • Published
spaces
12
Build error
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ChatBot: learn about André's research through an AI assistant
Running
1
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LungTumorMask: Automatic lung tumor segmentation in CT
Running
3
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LyNoS: automatic lymph node segmentation using deep learning
Sleeping
9
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AeroPath: automatic airway segmentation using deep learning
Sleeping
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ViT: Image Classifier
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vit-explainer
models
None public yet