# Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits

arXiv: https://arxiv.org/abs/2112.09496 · HTML: https://mlsanity.com/paper/2112.09496/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 101 references that are in the map.
- First posted on arXiv 17 Dec 2021.

## Builds on (most cited first, 25 of 25)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [Fixing Data Augmentation to Improve Adversarial Robustness](https://mlsanity.com/paper/2103.01946/) (2021)
- [Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis](https://mlsanity.com/paper/1912.08937/) (2019)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary](https://mlsanity.com/paper/2006.13932/) (2020)
- [Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations](https://mlsanity.com/paper/2008.05571/) (2020)
- [Deep Learning for Whole Slide Image Analysis: An Overview](https://mlsanity.com/paper/1910.11097/) (2019)
- [Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images](https://mlsanity.com/paper/1907.09478/) (2019)
- [NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation](https://mlsanity.com/paper/2102.09099/) (2021)
- [Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles](https://mlsanity.com/paper/2006.12117/) (2020)
- [Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations](https://mlsanity.com/paper/2106.13689/) (2021)
- [WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images](https://mlsanity.com/paper/2105.10445/) (2021)
- [All You Need is Color: Image based Spatial Gene Expression Prediction using Neural Stain Learning](https://mlsanity.com/paper/2108.10446/) (2021)
- [HistoTransfer: Understanding Transfer Learning for Histopathology](https://mlsanity.com/paper/2106.07068/) (2021)

## Built on by (most cited first, 2 of 2)

- [An Aggregation of Aggregation Methods in Computational Pathology](https://mlsanity.com/paper/2211.01256/) (2022)
- [Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures](https://mlsanity.com/paper/2208.12587/) (2022)

## Abstract (arXiv)

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. Recent deep learning based developments in CPath have successfully leveraged sheer volume of raw pixel data in histology images for predicting target parameters in the domains of diagnostics, prognostics, treatment sensitivity and patient stratification -- heralding the promise of a new data-driven AI era for both histopathology and oncology. With data serving as the fuel and AI as the engine, CPath algorithms are poised to be ready for takeoff and eventual launch into clinical and pharmaceutical orbits. In this paper, we discuss CPath limitations and associated challenges to enable the readers distinguish hope from hype and provide directions for future research to overcome some of the major challenges faced by this budding field to enable its launch into the two orbits.