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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.
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.
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.
Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images
Digital histology images are amenable to the application of convolutional neural network (CNN) for analysis due to the sheer size of pixel data present in them.
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.
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Colorectal Gland Segmentation:
on CRAG
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.