Summary of Beyond Labels: a Self-supervised Framework with Masked Autoencoders and Random Cropping For Breast Cancer Subtype Classification, by Annalisa Chiocchetti et al.
Beyond Labels: A Self-Supervised Framework with Masked Autoencoders and Random Cropping for Breast Cancer Subtype Classification
by Annalisa Chiocchetti, Marco Dossena, Christopher Irwin, Luigi Portinale
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research paper proposes a novel approach for breast cancer sub-type classification using histopathological images, leveraging masked autoencoders (MAEs) to learn self-supervised embeddings. The MAE captures informative representations of histopathological data, enabling feature learning without extensive labeled datasets. During pre-training, the researchers investigate generating a large dataset from whole-slide images (WSIs) automatically using random crop techniques. They also assess the performance of linear probes for multi-class classification tasks of cancer sub-types using the learned embeddings. The proposed approach aims to achieve strong performance on downstream tasks by combining the strengths of vision transformers (ViTs) and autoencoders. The model is evaluated on the BRACS dataset, with results compared to existing benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps doctors better identify different types of breast cancer from pictures taken during biopsies. They use a special kind of AI called masked autoencoders (MAEs) to learn how to recognize patterns in these images without needing lots of labeled data. The team also tries out a new way to automatically generate lots of training data from whole-slide images. Their goal is to develop an AI that can accurately identify different types of breast cancer, which could lead to better treatment options for patients. |
Keywords
» Artificial intelligence » Classification » Mae » Self supervised
