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Found 22 papers, 7 papers with code
Hitchhiker's guide to cancer-associated lymphoid aggregates in histology images: manual and deep learning-based quantification approaches
Quantification of lymphoid aggregates including tertiary lymphoid structures with germinal centers in histology images of cancer is a promising approach for developing prognostic and predictive tissue biomarkers.
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.
HoVer-UNet: Accelerating HoVerNet with UNet-based multi-class nuclei segmentation via knowledge distillation
We present HoVer-UNet, an approach to distill the knowledge of the multi-branch HoVerNet framework for nuclei instance segmentation and classification in histopathology.
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.
Seeded iterative clustering for histology region identification
Annotations are necessary to develop computer vision algorithms for histopathology, but dense annotations at a high resolution are often time-consuming to make.
Mitosis domain generalization in histopathology images -- The MIDOG challenge
The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms.
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.
Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations
Global annotations refer to global findings in the high-resolution image and do not include information about the location of the regions of interest or the magnification levels used to identify a finding.
Quantifying the Scanner-Induced Domain Gap in Mitosis Detection
Hypothesizing that the scanner device plays a decisive role in this effect, we evaluated the susceptibility of a standard mitosis detection approach to the domain shift introduced by using a different whole slide scanner.
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.
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.
Virtual staining for mitosis detection in Breast Histopathology
We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa.
Needles in Haystacks: On Classifying Tiny Objects in Large Images
In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images.
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).
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of $\kappa$ = 0. 567, 95% CI [0. 464, 0. 671] between the predicted scores and the ground truth.
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.
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.
Towards automatic pulmonary nodule management in lung cancer screening with deep learning
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy.
