computer-vision-8

whole slide images

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in whole-slide-images-8

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Dataset

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Libraries

i

Use these libraries to find whole-slide-images-8 models and implementations

jamesdolezal/slideflow

3 papers 213

Datasets

PanNuke

CryoNuSeg

LKS

MITOS_WSI_CMC

Subtasks

No subtasks available.

Most implemented papers

PanNuke Dataset Extension, Insights and Baselines

N. Rajpoot, Jevgenij Gamper, Navid Alemi Koohbanani, S. Graham, M. Jahanifar, S. Khurram, A. Azam, K. Hewitt•Mon Mar 23 2020

This work study the performance of segmentation and classification models when applied to the proposed dataset and demonstrate the application of models trained on PanNuke to whole-slide images.

221

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Deep Learning for Identifying Metastatic Breast Cancer

Andrew H. Beck, Dayong Wang, A. Khosla, Rishab Gargeya, H. Irshad•Fri Jun 17 2016

The power of using deep learning to produce significant improvements in the accuracy of pathological diagnoses is demonstrated, by combining the deep learning system's predictions with the human pathologist's diagnoses.

976 0

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Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides

A. Maier, M. Aubreville, C. Bertram, C. Marzahl, R. Klopfleisch, J. Stayt, A. Jasensky, F. Bartenschlager, Marco Fragoso-Garcia, A. Barton, Svenja Elsemann, S. Jabari, Jens Krauth, Prathmesh Madhu, J. Voigt, Jenny Hill•Sun Aug 11 2019

This work proposes an automated object detection pipeline, enabling accurate, reproducible and quick EIPH scoring in WSI and evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts.

49 0

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A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

Jiahao Lu, Natavsa Sladoje, Christina Runow Stark, E. D. Ramqvist, J. Hirsch, Joakim Lindblad•Mon Sep 30 2019

This work proposes a fully automated pipeline for oral cancer detection on whole slide cytology images that consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification.

32 0

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EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control

A. Maier, M. Aubreville, C. Bertram, C. Marzahl, R. Klopfleisch, J. Voigt, Jennifer K. Maier, Christian Bergler, Christine Kröger, Katharina Breininger•Wed Apr 29 2020

The open-source online platform EXACT (EXpert Algorithm Collaboration Tool) is developed that enables the collaborative interdisciplinary analysis of images from different domains online and offline and has already been successfully applied to a broad range of annotation tasks.

40 0

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Dataset on Bi- and Multi-Nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors

A. Maier, M. Aubreville, C. Bertram, T. Donovan, C. Marzahl, R. Klopfleisch, F. Bartenschlager, Katharina Breininger, M. Tecilla, Marco Fragoso, Frauke Wilm•Mon Jan 04 2021

This research presents a novel probabilistic procedure called “spot-spot PCR” that can be used to characterize the structure of the connective tissue of the immune system and provide real-time information about the immune systems of animals.

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Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning

Yin Li, Bin Li, K. Eliceiri•Mon Nov 16 2020

This work proposes a MIL-based method for WSI classification and tumor detection that does not require localized annotations, and introduces a novel MIL aggregator that models the relations of the instances in a dual-stream architecture with trainable distance measurement.

838 0

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Fast and Scalable Image Search For Histology

Ming Y. Lu, Tiffany Y. Chen, Drew F. K. Williamson, Faisal Mahmood, Chengkuan Chen, A. J. Schaumberg•Tue Jul 27 2021

Fast Image Search for Histopathology (FISH), a histology image search pipeline that is infinitely scalable and achieves constant search speed that is independent of the image database size while being interpretable and without requiring detailed annotations is presented.

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Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology

I. Reinertsen, E. Smistad, A. Pedersen, H. S. Pettersen, I. Belevich, E. S. Røyset, E. Jokitalo, I. Bakke•Mon Nov 15 2021

A code-free pipeline utilizing free-to-use, open-source software for creating and deploying deep learning-based segmentation models for computational pathology is presented and pathologists without programming experience can create near state-of-the-art segmentation solutions for histopathological WSIs using only free- to-use software.

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