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Found 28 papers, 10 papers with code
From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review
We aim to provide a comprehensive understanding of the available approaches and their application for various purposes.
Large Multimodal Model based Standardisation of Pathology Reports with Confidence and their Prognostic Significance
In this work, we present a practical approach based on the use of large multimodal models (LMMs) for automatically extracting information from scanned images of pathology reports with the goal of generating a standardised report specifying the value of different fields along with estimated confidence about the accuracy of the extracted fields.
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.
Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training
Quantum machine learning with quantum kernels for classification problems is a growing area of research.
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.
Synthesis of Annotated Colorectal Cancer Tissue Images from Gland Layout
Generating realistic tissue images with annotations is a challenging task that is important in many computational histopathology applications.
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.
MesoGraph: Automatic Profiling of Malignant Mesothelioma Subtypes from Histological Images
Tissue is represented by a cell graph with both cell-level morphological and regional features.
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.
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.
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.
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.
Insights into performance evaluation of com-pound-protein interaction prediction methods
We also show that random pairing for gen-erating synthetic negative examples for training and performance evaluation results in models with better generalization performance in comparison to more sophisticated strategies used in existing studies.
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.
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.
L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy naïve breast cancer patients
Can we predict if an early stage cancer patient is at high risk of developing distant metastasis and what clinicopathological factors are associated with such a risk?
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.
Elementary Effects Analysis of factors controlling COVID-19 infections in computational simulation reveals the importance of Social Distancing and Mask Usage
COVID-19 was declared a pandemic by the World Health Organization (WHO) on March 11th, 2020.
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.
AMP0: Species-Specific Prediction of Anti-microbial Peptides using Zero and Few Shot Learning
Our computational cross-validation results show that the pro-posed scheme is particularly effective for targeted antimicrobial prediction in comparison to existing approaches and can be used for screening potential antimicrobial peptides in a targeted manner especially for cases in which the number of training examples is small.
Ten ways to fool the masses with machine learning
If you want to tell people the truth, make them laugh, otherwise they'll kill you.
Machine Learning with Abstention for Automated Liver Disease Diagnosis
For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect.
