Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images

“Generative adversarial nets,”

Ian Goodfellow et al., · 2014

Earlier work this paper cites.

“Neural machine translation by jointly learning to align and translate,”

Dzmitry Bahdanau et al., · 2015

Earlier work this paper cites.

“Deep residual learning for image recognition,”

Kaiming He et al., · 2016

Earlier work this paper cites.

“Image-to-image translation with conditional adversarial networks,”

Phillip Isola et al., · 2017

Earlier work this paper cites.

“Attention-based deep multiple instance learning,”

Maximilian Ilse et al., · 2018

Cited alongside, same era.

“Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology,”

D. Tellez et al., · 2019

Cited alongside, same era.

“AI-based pathology predicts origins for cancers of unknown primary,”

Ming Y Lu et al., · 2021

Cited alongside, same era.

“Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study,”

Mohsin Bilal, Shan E Ahmed Raza, Ayesha Azam, Simon Graham, Mohammad Ilyas, Ian A Cree, David Snead, Fayyaz Minhas, and Nasir M Rajpoot, · 2021

Cited alongside, same era.

“Data-efficient and weakly supervised computational pathology on whole-slide images,”

Ming Y Lu et al., · 2021

Later among the works it cites.

“Self-path: Self-supervision for classification of pathology images with limited annotations,”

Navid Alemi Koohbanani, Balagopal Unnikrishnan, Syed Ali Khurram, Pavitra Krishnaswamy, and Nasir Rajpoot, · 2021

Later among the works it cites.

“Transmil: Transformer based correlated multiple instance learning for whole slide image classification,”

Original

Zhuchen others Shao, · 2021

Later among the works it cites.

“Scaling vision transformers to gigapixel images via hierarchical self-supervised learning,”

Richard J Chen et al., · 2022

Closest in time.