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Awesome-cell-detection-segmentation

Nucleus/cell detection and segmentation on microscopy images and digital pathology.

Overview paper

  • Robust NucleusCell Detection and Segmentation in Digital Pathology and Microscopy Images A Comprehensive Review[paper]
  • Computational Pathology: Challenges and Promises for Tissue Analysis.Thomas J. Fuchsa,b, Joachim M. Buhmann.2017[paper]

Classification

1.Auto-encoder

  • Classification of Histology Sections via Multispectral Convolutional Sparse Coding[paper][code]
  • Stacked Predictive Sparse Decomposition for Classification of Histology Sections. Hang Chang. 2015paper[code]
  • Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstrcution and Stacked Denoising Autoencoders. MICCAI15 [paper]
  • Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection 2016[paper]
  • Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images. [paper]

2.CNN

  • HEp-2 Cell Image Classification with Deep Convolutional Neural Networks.2014[paper]
  • Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning. 2016[paper]
  • A Guided Spatial Transformer Network for Histology Cell Differentiation. 2017[paper]
  • Transitioning between Convolutional and Fully Connected Layers in Neural Networks. 2017[paper]
  • DeepPap: Deep Convolutional Networks for Cervical Cell Classification.2018 [paper]

Histopathology

  • Classification of Breast Cancer Histology using Deep Learning[paper]
  • Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach [paper]
  • Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis [papercode]
  • An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer Tissue Microarray[paper]
  • Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification[paper]
  • Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. 2018[papercode]
  • Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images.2019 [papercode]

Detection

1.CNN

  • Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks. miccai 2013[paper][code]
  • Deep Learning Based Automatic Immune Cell Detection for Immunohistochemistry Images. 2014[paper]
  • Microscopy cell counting and detection with fully convolutional regression networks. 2015[paper][code]
  • Beyond Classification Structured Regression for Robust Cell Detection Using Convolutional Neural Network. 2015[paper]
  • Cell Counting by Regression Using Convolutional Neural Network. 2016[paper]
  • Deep Convolutional Neural Networks for Human Embryonic Cell Counting. 2016[paper]
  • Deep Learning for Imaging Flow Cytometry:Cell Cycle Analysis of Jurkat Cells. 2016[paper]
  • AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images. 2016[paper]
  • Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images. 2016[paper]
  • Deep Learning for Identifying Metastatic Breast Cancer. 2016[paper]
  • Detecting Cancer Metastases on Gigapixel Pathology Images. 2017 google[paper]
  • Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. 2017[paper]
  • MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network. CVPR 2017[paper][code]

2.Random_forest

  • Learning-based mitotic cell detection in histopathological images. 2012 [paper]
  • method: random forest classify object and non-object, and svm classify last.
  • You Should Use Regression to Detect Cells. Philipp KainzMartin Urschler.In MICCAI. 2015.[paper][code]
  • Methd: ACF feature, sliding window, random trees regression.

3.SVM

  • Learning to detect cells using non-overlapping extremal regions. 2012.[paper][code]
  • Method: MSER,DP,structure SVM.

4.CRF

  • Heterogeneous Conditional Random Field_ Realizing Joint Detection and Segmentation of Cell Regions in Microscopic Images. cvpr2010.[paper]

Segmentation

1.CNN

  • Nuclei Segmentation via Sparsity Constrained Convoluational Regression. 2015[paper][code]
  • Cell Segmentation Proposal Network For Microscopy Image Analysis. 2016[paper]
  • DCAN Deep Contour-Aware Networks for Accurate Gland Segmentation. CVPR 2016[paper]
  • Constrained Deep Weak Supervision for Histopathology Image Segmentation. 2017[paper]
  • Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation. 2017 [paper]
  • TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References.2017 [paper]
  • Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images [paper code]
  • MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images.2018 [paper]

2.Level Set

  • Automated Nucleus and Cytoplasm Segmentation of Overlapping Cervical Cells. 2013[paper][code]
  • An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells. 2015[paper][code]

Feature

  • Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images.2020 [papercode]

GAN

  • Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks. 2017 [paper]

Super-Resolution

  • Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy.2018 [paper]

Reconstruct

  • Reconstructing cell cycle and disease progression using deep learning [paper]

Datasets

SoftWare

Reference