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Found 29 papers, 10 papers with code
Benchmarking Domain Generalization Algorithms in Computational Pathology
Deep learning models have shown immense promise in computational pathology (CPath) tasks, but their performance often suffers when applied to unseen data due to domain shifts.
TIAViz: A Browser-based Visualization Tool for Computational Pathology Models
Throughout the development of a machine learning (ML) model in digital pathology, it is crucial to have flexible, openly available tools to visualize models, from their outputs and predictions to the underlying annotations and images used to train or test a model.
An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer
Tumour-infiltrating lymphocytes (TILs) are considered as a valuable prognostic markers in both triple-negative and human epidermal growth factor receptor 2 (HER2) positive breast cancer.
Domain Generalization in Computational Pathology: Survey and Guidelines
Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis applications.
Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge
The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains.
A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia
To address this, we developed a novel artificial intelligence algorithm that can assign an Oral Malignant Transformation (OMT) risk score, based on histological patterns in the in Haematoxylin and Eosin stained whole slide images, to quantify the risk of OED progression.
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.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
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.
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.
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.
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.
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.
Simultaneous Nuclear Instance and Layer Segmentation in Oral Epithelial Dysplasia
Oral epithelial dysplasia (OED) is a pre-malignant histopathological diagnosis given to lesions of the oral cavity.
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.
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.
Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks
This framework has two essential parts of tendon segmentation and classification.
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.
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.
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.
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.
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.
Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images
In order to detect the lesion in the presence of these problems, we propose a supervised saliency detection method tailored for dermoscopic images based on the discriminative regional feature integration (DRFI).
