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Summary of Source-free Domain Adaptation Of Weakly-supervised Object Localization Models For Histology, by Alexis Guichemerre et al.


Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology

by Alexis Guichemerre, Soufiane Belharbi, Tsiry Mayet, Shakeeb Murtaza, Pourya Shamsolmoali, Luke McCaffrey, Eric Granger

First submitted to arxiv on: 29 Apr 2024

Categories

  • Main: Computer Vision and Pattern Recognition (cs.CV)
  • Secondary: Machine Learning (cs.LG)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper presents a study on source-free domain adaptation of weakly supervised object localization models in digital pathology. The goal is to adapt a pre-trained model trained on labeled data to new, unlabeled target data without using any source data, ensuring privacy and efficiency. The focus is on adapting WSOL models for both classification and localization tasks, which raises several challenges. Four state-of-the-art SFDA methods are compared in terms of classification and localization accuracy on the Glas and Camelyon16 histology datasets.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper helps doctors better diagnose cancer from images by teaching a computer to adapt its skills without needing more labeled data. The computer model is trained on some images, then can be used on new, different images without needing any of the original data. This makes it faster and more private. The study compares four ways to do this adaptation and finds that they all struggle with localizing specific parts of the image after adapting.

Keywords

» Artificial intelligence  » Classification  » Domain adaptation  » Supervised