Fetching the paper…
Reading the bibliography…
The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice.
An efficient k-means clustering algorithm
Khaled Alsabti, Sanjay Ranka, and Vineet Singh · 1997
Earlier work this paper cites.
The use of the area under the roc curve in the evaluation of machine learning algorithms
Andrew P Bradley · 1997
Earlier work this paper cites.
Multiple mutations and cancer
Lawrence A Loeb, Keith R Loeb, and Jon P Anderson · 2003
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Patch-based convolutional neural network for whole slide tissue image classification
Le Hou, Dimitris Samaras, Tahsin M Kurc, Yi Gao, James E Davis, and Joel H Saltz · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
Earlier work this paper cites.
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen AWM Van Der Laak, Meyke Hermsen, Quirine F Manson, Maschenka Balkenhol, et al · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Earlier work this paper cites.
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
Nicolas Coudray, Paolo Santiago Ocampo, Theodore Sakellaropoulos, Navneet Narula, Matija Snuderl, David Fenyö, Andre L Moreira, Narges Razavian, and Aristotelis Tsirigos · 2018
Earlier work this paper cites.
The cancer genome atlas: creating lasting value beyond its data
Carolyn Hutter and Jean Claude Zenklusen · 2018
Earlier work this paper cites.
Attention-based deep multiple instance learning
Maximilian Ilse, Jakub Tomczak, and Max Welling · 2018
Earlier work this paper cites.
Attention-based deep multiple instance learning
Maximilian Ilse, Jakub Tomczak, and Max Welling · 2018
Earlier work this paper cites.
Path r-cnn for prostate cancer diagnosis and gleason grading of histological images
Wenyuan Li, Jiayun Li, Karthik V Sarma, King Chung Ho, Shiwen Shen, Beatrice S Knudsen, Arkadiusz Gertych, and Corey W Arnold · 2018
Earlier work this paper cites.
Few-shot image recognition by predicting parameters from activations
Siyuan Qiao, Chenxi Liu, Wei Shen, and Alan L Yuille · 2018
Earlier work this paper cites.
Deep convolutional neural networks for breast cancer histology image analysis
Alexander Rakhlin, Alexey Shvets, Vladimir Iglovikov, and Alexandr A Kalinin · 2018
Earlier work this paper cites.
Task agnostic meta-learning for few-shot learning
Muhammad Abdullah Jamal and Guo-Jun Qi · 2019
Earlier work this paper cites.
Deep learning-based gleason grading of prostate cancer from histopathology images—role of multiscale decision aggregation and data augmentation
Davood Karimi, Guy Nir, Ladan Fazli, Peter C Black, Larry Goldenberg, and Septimiu E Salcudean · 2019
Earlier work this paper cites.
Characteristics of the tissue section that influence the staining outcome in immunohistochemistry
Sylwia Libard, Dijana Cerjan, and Irina Alafuzoff · 2019
Earlier work this paper cites.
Digital pathology and artificial intelligence
Muhammad Khalid Khan Niazi, Anil V Parwani, and Metin N Gurcan · 2019
Earlier work this paper cites.
Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine · 2019
Earlier work this paper cites.
Patch-based system for classification of breast histology images using deep learning
Kaushiki Roy, Debapriya Banik, Debotosh Bhattacharjee, and Mita Nasipuri · 2019
Cited alongside, same era.
Learning to model relationships for zero-shot video classification
Junyu Gao, Tianzhu Zhang, and Changsheng Xu · 2020
Cited alongside, same era.
Few-shot learning via embedding adaptation with set-to-set functions
Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha · 2020
Cited alongside, same era.
Generalizing few-shot classification of whole-genome doubling across cancer types
Sherry Chao and David Belanger · 2021
Cited alongside, same era.
Multi-prototype few-shot learning in histopathology
Jessica Deuschel, Daniel Firmbach, Carol I Geppert, Markus Eckstein, Arndt Hartmann, Volker Bruns, Petr Kuritcyn, Jakob Dexl, David Hartmann, Dominik Perrin, et al · 2021
Cited alongside, same era.
Multi-prototype few-shot learning in histopathology
Artifact identification in digital histopathology images using few-shot learning
Nazim N Shaikh, Kamil Wasag, and Yao Nie · 2022
Later among the works it cites.
Tip-adapter: Training-free adaption of clip for few-shot classification
Renrui Zhang, Wei Zhang, Rongyao Fang, Peng Gao, Kunchang Li, Jifeng Dai, Yu Qiao, and Hongsheng Li · 2022
Later among the works it cites.
Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
Later among the works it cites.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Later among the works it cites.
Vectorized evidential learning for weakly-supervised temporal action localization
Junyu Gao, Mengyuan Chen, and Changsheng Xu · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jessica Deuschel, Daniel Firmbach, Carol I Geppert, Markus Eckstein, Arndt Hartmann, Volker Bruns, Petr Kuritcyn, Jakob Dexl, David Hartmann, Dominik Perrin, et al · 2021
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
Cited alongside, same era.
Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning
Bin Li, Yin Li, and Kevin W Eliceiri · 2021
Cited alongside, same era.
Dt-mil: deformable transformer for multi-instance learning on histopathological image
Hang Li, Fan Yang, Yu Zhao, Xiaohan Xing, Jun Zhang, Mingxuan Gao, Junzhou Huang, Liansheng Wang, and Jianhua Yao · 2021
Cited alongside, same era.
Dt-mil: deformable transformer for multi-instance learning on histopathological image
Hang Li, Fan Yang, Yu Zhao, Xiaohan Xing, Jun Zhang, Mingxuan Gao, Junzhou Huang, Liansheng Wang, and Jianhua Yao · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Transmil: Transformer based correlated multiple instance learning for whole slide image classification
Zhuchen Shao, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang, Xiangyang Ji, et al · 2021
Cited alongside, same era.
Exploring rich semantics for open-set action recognition
Yufan Hu, Junyu Gao, Jianfeng Dong, Bin Fan, and Hongmin Liu · 2023
Later among the works it cites.
A visual–language foundation model for pathology image analysis using medical twitter
Zhi Huang, Federico Bianchi, Mert Yuksekgonul, Thomas J Montine, and James Zou · 2023
Later among the works it cites.
Clipath: Fine-tune clip with visual feature fusion for pathology image analysis towards minimizing data collection efforts
Zhengfeng Lai, Zhuoheng Li, Luca Cerny Oliveira, Joohi Chauhan, Brittany N Dugger, and Chen-Nee Chuah · 2023
Later among the works it cites.
A learnable self-supervised task for unsupervised domain adaptation on point cloud classification and segmentation
Shaolei Liu, Xiaoyuan Luo, Kexue Fu, Manning Wang, and Zhijian Song · 2023
Later among the works it cites.
Negative instance guided self-distillation framework for whole slide image analysis
Xiaoyuan Luo, Linhao Qu, Qinhao Guo, Zhijian Song, and Manning Wang · 2023
Later among the works it cites.
Boosting 3d point cloud registration by transferring multi-modality knowledge
Mingzhi Yuan, Xiaoshui Huang, Kexue Fu, Zhihao Li, and Manning Wang · 2023
Later among the works it cites.
Proteinmae: masked autoencoder for protein surface self-supervised learning
Mingzhi Yuan, Ao Shen, Kexue Fu, Jiaming Guan, Yingfan Ma, Qin Qiao, and Manning Wang · 2023
Later among the works it cites.
Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners
Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Yu Qiao, Peng Gao, and Hongsheng Li · 2023
Later among the works it cites.
Text-guided foundation model adaptation for pathological image classification
Yunkun Zhang, Jin Gao, Mu Zhou, Xiaosong Wang, Yu Qiao, Shaoting Zhang, and Dequan Wang · 2023
Later among the works it cites.
Not all features matter: Enhancing few-shot clip with adaptive prior refinement
Xiangyang Zhu, Renrui Zhang, Bowei He, Aojun Zhou, Dong Wang, Bin Zhao, and Peng Gao · 2023
Later among the works it cites.
Clip-adapter: Better vision-language models with feature adapters
Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, and Yu Qiao · 2024
Closest in time.
A visual-language foundation model for computational pathology
Ming Y Lu, Bowen Chen, Drew FK Williamson, Richard J Chen, Ivy Liang, Tong Ding, Guillaume Jaume, Igor Odintsov, Long Phi Le, Georg Gerber, et al · 2024
Closest in time.
The rise of ai language pathologists: Exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification
Linhao Qu, Kexue Fu, Manning Wang, Zhijian Song, et al · 2024
Closest in time.
Collaborative consortium of foundation models for open-world few-shot learning
Shuai Shao, Yu Bai, Yan Wang, Baodi Liu, and Bin Liu · 2024
Closest in time.
Deil: Direct-and-inverse clip for open-world few-shot learning
Shuai Shao, Yu Bai, Yan Wang, Baodi Liu, and Yicong Zhou · 2024
Closest in time.
Feature re-embedding: Towards foundation model-level performance in computational pathology
Wenhao Tang, Fengtao Zhou, Sheng Huang, Xiang Zhu, Yi Zhang, and Bo Liu · 2024
Closest in time.