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Found 7 papers, 1 papers with code
Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels
As such, developing algorithms which do not require manual pixel-wise annotations, but can learn using only the clinical report would be a significant advancement for the field.
Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists
We investigated the value of AI assistance for grading prostate biopsies.
Automated Gleason Grading of Prostate Biopsies using Deep Learning
We developed a fully automated deep learning system to grade prostate biopsies.
Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification
Results show that semi-/unsupervised methods have an advantage over supervised learning when few labels are available.
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 laboratories stain tissue slides that exhibit similar but not identical color appearance.
Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
The H&E slides were co-registered to the IHC slides.
Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders
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.
