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Found 27 papers, 6 papers with code
An AI based Digital Score of Tumour-Immune Microenvironment Predicts Benefit to Maintenance Immunotherapy in Advanced Oesophagogastric Adenocarcinoma
Gastric and oesophageal (OG) cancers are the leading causes of cancer mortality worldwide.
Cell Maps Representation For Lung Adenocarcinoma Growth Patterns Classification In Whole Slide Images
Lung adenocarcinoma is a morphologically heterogeneous disease, characterized by five primary histologic growth patterns.
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.
Transformer-based Model for Oral Epithelial Dysplasia Segmentation
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity.
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.
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.
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.
Consistency Regularisation in Varying Contexts and Feature Perturbations for Semi-Supervised Semantic Segmentation of Histology Images
The proposed method incorporates context-aware consistency by contrasting pairs of overlapping images in a pixel-wise manner from changing contexts resulting in robust and context invariant features.
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.
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.
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.
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.
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.
FedDropoutAvg: Generalizable federated learning for histopathology image classification
In this study, we propose FedDropoutAvg, a new federated learning approach for training a generalizable model.
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 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.
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.
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.
HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification
The model is trained in multi-task learning manner with noise tolerant joint loss for classification localization and achieves better performance when given limited data in contrast to a simple deep model.
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
DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images
Subsequently, seeds generated from cell segmentation were propagated to a spatially constrained convolutional neural network for the classification of the cells into stromal, lymphocyte, Ki67-positive cancer cell, and Ki67-negative cancer cell.
Deconvolving convolution neural network for cell detection
Output of the trained CNN is then deconvolved to generate points as cell detection.
Micro-Net: A unified model for segmentation of various objects in microscopy images
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images.
Ranked #13 on
Multi-tissue Nucleus Segmentation
on Kumar
