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Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels
Prostate cancer is the most prevalent cancer among men in Western countr...
06/05/2020 ∙ by Hans Pinckaers, et al. ∙
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Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists
While the Gleason score is the most important prognostic marker for pros...
02/11/2020 ∙ by Wouter Bulten, et al. ∙
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Streaming convolutional neural networks for end-to-end learning with multi-megapixel images
Due to memory constraints on current hardware, most convolution neural n...
11/11/2019 ∙ by Hans Pinckaers, et al. ∙
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Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands
Automated medical image segmentation plays a key role in quantitative re...
10/23/2019 ∙ by Hans Pinckaers, et al. ∙
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Automated Gleason Grading of Prostate Biopsies using Deep Learning
The Gleason score is the most important prognostic marker for prostate c...
07/18/2019 ∙ by Wouter Bulten, et al. ∙
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Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification
Large amounts of unlabelled data are commonplace for many applications i...
05/16/2019 ∙ by Koen Dercksen, et al. ∙
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A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Semantic segmentation of medical images aims to associate a pixel with a...
02/25/2019 ∙ by Amber L. Simpson, et al. ∙
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Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
Stain variation is a phenomenon observed when distinct pathology laborat...
02/18/2019 ∙ by David Tellez, et al. ∙
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Neural Image Compression for Gigapixel Histopathology Image Analysis
We present Neural Image Compression (NIC), a method to reduce the size o...
11/07/2018 ∙ by David Tellez, et al. ∙
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Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
Manual counting of mitotic tumor cells in tissue sections constitutes on...
08/17/2018 ∙ by David Tellez, et al. ∙
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Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
Prostate cancer (PCa) is graded by pathologists by examining the archite...
08/17/2018 ∙ by Wouter Bulten, et al. ∙
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Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders
We propose an unsupervised method using self-clustering convolutional ad...
04/19/2018 ∙ by Wouter Bulten, et al. ∙
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Training convolutional neural networks with megapixel images
To train deep convolutional neural networks, the input data and the inte...
04/16/2018 ∙ by Hans Pinckaers, et al. ∙
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Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Aim: Early detection and correct diagnosis of lung cancer are the most i...
03/14/2018 ∙ by Zhang Li, et al. ∙
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Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
Automated classification of histopathological whole-slide images (WSI) o...
05/10/2017 ∙ by Babak Ehteshami Bejnordi, et al. ∙
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Comparison of Different Methods for Tissue Segmentation in Histopathological Whole-Slide Images
Tissue segmentation is an important pre-requisite for efficient and accu...
03/17/2017 ∙ by Péter Bándi, et al. ∙
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Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
The anatomical location of imaging features is of crucial importance for...
10/16/2016 ∙ by Mohsen Ghafoorian, et al. ∙
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