Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

By Korsuk Sirinukunwattana, Shan E Ahmed Raza, Yee-Wah Tsang, David R. J. Snead, Ian A. Cree, Nasir M. Rajpoot. IEEE Transactions on Medical Imaging, 2016

1,147 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7256088eece603df2e5675025e8bed90c0f21171

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images (2018, positive): “The CNN is a supervised deep learning method that has been successfully applied in a large number of image analysis problems (Ciresxan et al., 2013; Huang et al., 2016; Xie et al., 2015a,2015b;Wang et al., 2016; Sirinukunwattana et al., 2016; Bayramoglu and Heikkila, 2016; Su et al., 2015; Hou et al”
Target-Independent Domain Adaptation for WBC Classification Using Generative Latent Search (2020, methodology): “In SC-CNN [9], a weighted sum of multiple classifiers is used to predict the class label of cell nuclei detected with a Spatially Constrained CNN.”
Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification (2019, methodology): “, natural and bio-medical images) of datasets like CIFAR-10 and CIFAR-100 [18], Tiny ImageNet [21], and CRCHistoPhenotypes [22].”
CellViT: Vision Transformers for Precise Cell Segmentation and Classification (2023, methodology): “5 criterion to find matching instances ( y , ˆ y ) between GT-instances and predictions for the detection task, we use the methodology of Sirinukunwat-tana et al. [70] and define a match ( y , ˆ y ) if both centers of mass are within a radius of 6 px (0 .”
Image analysis and machine learning in digital pathology: Challenges and opportunities (2016, positive): “recently showed the use of locality sensitive deep learning to automatically detect and classify individual nuclei in routine colon cancer histology images (Sirinukunwattana et al., 2016).”
Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images (2018, background): “Note, because SC-CNN does not produce a segmentation mask, we do not report the PQ for this method.”
Methods for Segmentation and Classification of Digital Microscopy Tissue Images (2018, background): “Methods for segmentation and classification have been proposed by several research projects (Xing and Yang 2016; Peikari and Martel, 2016; Zheng et al. 2017; Gurcan et al., 2009; Senaras, 2018; Chen et al, 2017; Wang et al. 2016; Xie et al. 2015; Sirinukunwattana et al. 2016; Ghaznavi et al., 2013; ”
Efficient Classification of Very Large Images with Tiny Objects (2021, methodology): “We present experiments on five datasets: i) the colon cancer dataset introduced in [42] aims to detect whether epithelial cells exist in a Hematoxylin and Eosin (H&E) stained images.”
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, background): “Sirinukuwattana et al. [139] proposed a post-processing based on neighbors to classify colon cancer histology images.”
Attention-based Deep Multiple Instance Learning (2018, neutral): “In the following, we perform two experiments on classifying weakly-labeled real-life histopathology images of the breast cancer dataset (B REAST CANCER ) (Gelasca et al., 2008) and the colon cancer dataset (C OLON CANCER ) (Sirinukunwattana et al., 2016).”