15.4k citations · 20.7k across the 40 of their papers we have counts for
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Neural Image Compression for Gigapixel Histopathology Image Analysis
David Tellez, Geert Litjens, Jeroen van der Laak +1
We propose Neural Image Compression (NIC), a two-step method to build convolutional neural networks for gigapixel image analysis solely using weak image-level labels. First, gigapi…
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
David Tellez, Maschenka Balkenhol, Irene Otte-Holler +10
Manual counting of mitotic tumor cells in tissue sections constitutes one of the strongest prognostic markers for breast cancer. This procedure, however, is time-consuming and erro…
Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
Wouter Bulten, Péter Bándi, Jeffrey Hoven +7
Prostate cancer (PCa) is graded by pathologists by examining the architectural pattern of cancerous epithelial tissue on hematoxylin and eosin (H&E) stained slides. Given the impor…
Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders
Wouter Bulten, Geert Litjens
We propose an unsupervised method using self-clustering convolutional adversarial autoencoders to classify prostate tissue as tumor or non-tumor without any labeled training data.…
Training convolutional neural networks with megapixel images
Hans Pinckaers, Geert Litjens
To train deep convolutional neural networks, the input data and the intermediate activations need to be kept in memory to calculate the gradient descent step. Given the limited mem…
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Zhang Li, Zheyu Hu, Jiaolong Xu +10
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome. This study aims to assess which deep learning models perform be…