Summary of Compute-efficient Medical Image Classification with Softmax-free Transformers and Sequence Normalization, by Firas Khader et al.

Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization

by Firas Khader, Omar S. M. El Nahhas, Tianyu Han, Gustav Müller-Franzes, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn

First submitted to arxiv on: 3 Jun 2024

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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 The Transformer model has been instrumental in driving advancements in natural language processing, speech recognition, and computer vision. However, its quadratic computational and memory complexity relative to sequence length hinders its application to longer sequences, particularly crucial in medical imaging where high-resolution images can reach gigapixel scales. This paper addresses this issue by introducing a simple yet effective method that eliminates the softmax function from the attention mechanism, adopting sequence normalization techniques for key, query, and value tokens, and reordering matrix multiplications. This approach reduces memory- and compute complexity to a linear scale. The authors evaluate their approach across various medical imaging datasets, including fundoscopic, dermascopic, radiologic, and histologic imaging data. Results show that these models exhibit comparable performance to traditional Transformer models while efficiently handling longer sequences.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The Transformer model has been very important in helping machines understand language, recognize speech, and see images. One problem with this model is that it uses a lot of computer power and memory when dealing with long sequences of data. This can be a big issue when working with huge medical images. To solve this problem, the authors came up with a simple but effective way to make the Transformer model more efficient. They did this by removing a part of the model that wasn’t necessary, using new techniques to process the data, and rearranging how the computer does its calculations. This made the model use much less computer power and memory, making it possible to work with longer sequences.