Search Results for author:
Found 21 papers, 6 papers with code
Uncertainty-guided annotation enhances segmentation with the human-in-the-loop
Deep learning algorithms, often critiqued for their 'black box' nature, traditionally fall short in providing the necessary transparency for trusted clinical use.
Hierarchical Vision Transformers for Context-Aware Prostate Cancer Grading in Whole Slide Images
Vision Transformers (ViTs) have ushered in a new era in computer vision, showcasing unparalleled performance in many challenging tasks.
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset
Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.
Domain adaptation strategies for cancer-independent detection of lymph node metastases
Furthermore, we show the effectiveness of repeated adaptation of networks from one cancer type to another to obtain multi-task metastasis detection networks.
Automated risk classification of colon biopsies based on semantic segmentation of histopathology images
Artificial Intelligence (AI) can potentially support histopathologists in the diagnosis of a broad spectrum of cancer types.
Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathology
Between re-stained sections, the median registration error is 2. 3 {\mu}m and 0. 9 {\mu}m in the two subsets of the HyReCo dataset.
Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer
Nuclear pleomorphism, defined herein as the extent of abnormalities in the overall appearance of tumor nuclei, is one of the components of the three-tiered breast cancer grading.
HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images
We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks.
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.
Extending Unsupervised Neural Image Compression With Supervised Multitask Learning
Additionally, we trained multiple encoders with different training objectives, e. g. unsupervised and variants of MTL, and observed a positive correlation between the number of tasks in MTL and the system performance on the TUPAC16 dataset.
Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists
We investigated the value of AI assistance for grading prostate biopsies.
Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks
Furthermore, we show that the meta-loss function of M-heads improves OOD detection in terms of AUROC from 87. 4 to 88. 7.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Automated Gleason Grading of Prostate Biopsies using Deep Learning
We developed a fully automated deep learning system to grade prostate biopsies.
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.
Neural Image Compression for Gigapixel Histopathology Image Analysis
Second, a convolutional neural network (CNN) is trained on these compressed image representations to predict image-level labels, avoiding the need for fine-grained manual annotations.
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
Application of CNNs to hematoxylin and eosin (H&E) stained histological tissue sections is hampered by: (1) noisy and expensive reference standards established by pathologists, (2) lack of generalization due to staining variation across laboratories, and (3) high computational requirements needed to process gigapixel whole-slide images (WSIs).
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.
Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area.
Comparison of Different Methods for Tissue Segmentation in Histopathological Whole-Slide Images
Tissue segmentation is an important pre-requisite for efficient and accurate diagnostics in digital pathology.
The importance of stain normalization in colorectal tissue classification with convolutional networks
The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image.
Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images
Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture.
