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Found 61 papers, 17 papers with code
Synthesis of Annotated Colorectal Cancer Tissue Images from Gland Layout
Such synthetic images and their annotations can be useful in training and evaluation of algorithms in the domain of computational pathology.
Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification
There has been a surge of research in deep learning models for WSI classification with clinical applications such as cancer detection or prediction of molecular mutations from WSIs.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
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.
Nuclear Segmentation and Classification: On Color & Compression Generalization
Since the introduction of digital and computational pathology as a field, one of the major problems in the clinical application of algorithms has been the struggle to generalize well to examples outside the distribution of the training data.
SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts
Tissue image generation based on bespoke cellular layouts through the proposed framework allows users to generate different histological patterns from arbitrary topological arrangement of different types of cells.
An Aggregation of Aggregation Methods in Computational Pathology
Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels.
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images
Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations.
Multi-Scale Attention-based Multiple Instance Learning for Classification of Multi-Gigapixel Histology Images
To the best of our knowledge, this is the first attempt to predict LMP1 status on NPC using deep learning.
Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures
Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers.
IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification
The appearance of histopathology images depends on tissue type, staining and digitization procedure.
TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challenge
The Tumor InfiltratinG lymphocytes in breast cancER (TiGER) challenge, aims to assess the prognostic significance of computer-generated TILs scores for predicting survival as part of a Cox proportional hazards model.
Rank the triplets: A ranking-based multiple instance learning framework for detecting HPV infection in head and neck cancers using routine H&E images
Unique gene expression profiles were also identified with respect to HPV infection status, and is in line with existing findings.
Metrics reloaded: Recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
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.
One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification
In this paper, we present a multi-task learning approach for segmentation and classification of nuclei, glands, lumina and different tissue regions that leverages data from multiple independent data sources.
Deep Learning based Prediction of MSI using MMR Markers in Colorectal Cancer
Unlike previous studies on MSI prediction involving training a CNN using coarse labels (MSI vs Microsatellite Stable (MSS)), we have utilised fine-grain MMR labels for training purposes.
Deep Feature based Cross-slide Registration
During the slide preparation, a tissue section may be placed at an arbitrary orientation as compared to other sections of the same tissue block.
Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Images
Based on our experiments involving various datasets consisting of a total of 5, 306 WSIs, the results demonstrate that H2T based holistic WSI-level representations offer competitive performance compared to recent state-of-the-art methods and can be readily utilized for various downstream analysis tasks.
On Smart Gaze based Annotation of Histopathology Images for Training of Deep Convolutional Neural Networks
Unavailability of large training datasets is a bottleneck that needs to be overcome to realize the true potential of deep learning in histopathology applications.
REET: Robustness Evaluation and Enhancement Toolbox for Computational Pathology
Motivation: Digitization of pathology laboratories through digital slide scanners and advances in deep learning approaches for objective histological assessment have resulted in rapid progress in the field of computational pathology (CPath) with wide-ranging applications in medical and pharmaceutical research as well as clinical workflows.
Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits
Computational Pathology (CPath) is an emerging field concerned with the study of tissue pathology via computational algorithms for the processing and analysis of digitized high-resolution images of tissue slides.
CoNIC: Colon Nuclei Identification and Counting Challenge 2022
The challenge encourages researchers to develop algorithms that perform segmentation, classification and counting of nuclei within the current largest known publicly available nuclei-level dataset in CPath, containing around half a million labelled nuclei.
SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancer
The network was trained and tested on slides from The Cancer Genome Atlas (TCGA) in addition to two independent test datasets.
Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge
The detection of mitotic figures from different scanners/sites remains an important topic of research, owing to its potential in assisting clinicians with tumour grading.
Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input
From the simple measurement of tissue attributes in pathology workflow to designing an explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of tissue regions in histology images is a prerequisite.
Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification
The development of deep segmentation models for computational pathology (CPath) can help foster the investigation of interpretable morphological biomarkers.
A QuadTree Image Representation for Computational Pathology
The field of computational pathology presents many challenges for computer vision algorithms due to the sheer size of pathology images.
A Novel Cell Map Representation for Weakly Supervised Prediction of ER and PR Status from H&E WSIs
Digital pathology opens new pathways for computational algorithms to play a significant role in the prognosis, diagnosis, and analysis of cancer.
Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification
To tackle these challenges, we propose to use a statistical network analysis method to describe the complex structure of the tissue micro-environment by modelling nuclei and their connections as a network.
Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations
The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.
Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology
Deep learning models are routinely employed in computational pathology (CPath) for solving problems of diagnostic and prognostic significance.
A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma
In this study, our aim is to explore the prognostic significance of tumour-associated stroma infiltrating lymphocytes (TASILs) in head and neck squamous cell carcinoma (HNSCC) through an AI based automated method.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysis
In this paper, we propose a deep dictionary learning approach to solve the problem of tissue phenotyping in histology images.
Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles
We present ARCH, a computational pathology (CP) multiple instance captioning dataset to facilitate dense supervision of CP tasks.
Classification of COVID-19 via Homology of CT-SCAN
In this worldwide spread of SARS-CoV-2 (COVID-19) infection, it is of utmost importance to detect the disease at an early stage especially in the hot spots of this epidemic.
Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations
In this paper, we propose a self-supervised CNN approach to leverage unlabeled data for learning generalizable and domain invariant representations in pathology images.
SAFRON: Stitching Across the Frontier for Generating Colorectal Cancer Histology Images
Compared to other existing approaches, our framework is efficient in terms of the memory requirements for training and also in terms of the number of computations to construct a large high-resolution image.
NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images
As nuclei, cells and glands are fundamental objects for downstream analysis in computational pathology/cytology, in this paper we propose a simple CNN-based approach to speed up collecting annotations for these objects which requires minimum interaction from the annotator.
Multi-Task Learning in Histo-pathology for Widely Generalizable Model
In this work we show preliminary results of deep multi-task learning in the area of computational pathology.
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear.
Ranked #1 on
Breast Tumour Classification
on PCam
Breast Tumour Classification
Colorectal Gland Segmentation:
+3
PanNuke Dataset Extension, Insights and Baselines
The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides.
Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images
To train a robust deep learning model, one usually needs a balanced set of categories in the training data.
NuClick: From Clicks in the Nuclei to Nuclear Boundaries
Best performing nuclear segmentation methods are based on deep learning algorithms that require a large amount of annotated data.
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images
Furthermore, to deal with redundancy in the graph, we propose a sampling technique that removes nodes in areas of dense nuclear activity.
Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework
Spectral clustering method is applied on the output of the last SpaNet, which utilizes the nuclear mask and the Gaussian-like detection map for determining the connected components and associated cluster identifiers, respectively.
HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow.
Ranked #2 on
Multi-tissue Nucleus Segmentation
on CoNSeP
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis.
Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations
Computer-aided diagnosis systems for classification of different type of skin lesions have been an active field of research in recent decades.
Nuclei Detection Using Mixture Density Networks
Nuclei detection is an important task in the histology domain as it is a main step toward further analysis such as cell counting, cell segmentation, study of cell connections, etc.
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds
With multiple crowd gatherings of millions of people every year in events ranging from pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd analysis is emerging as a new frontier in computer vision.
Ranked #7 on
Crowd Counting
on UCF-QNRF
Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection
In this paper, we present a semi-supervised mitosis detection method which is designed to leverage a large number of unlabeled breast cancer 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.
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image analysis methods.
MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images
However, this task is non-trivial due to the large variability in glandular appearance and the difficulty in differentiating between certain glandular and non-glandular histological structures.
Ranked #4 on
Colorectal Gland Segmentation:
on CRAG
Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features
In this work, we propose a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs).
Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images
Convolutional neural networks (CNNs) have been recently used for a variety of histology image analysis.
Classification
Classification Of Breast Cancer Histology Images
+1
Novel digital tissue phenotypic signatures of distant metastasis in colorectal cancer
Distant metastasis is the major cause of death in colorectal cancer (CRC).
Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues
In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring.
