Deep is a Luxury We Don't Have
This work addresses computational efficiency for medical imaging tasks, though it appears incremental as it builds on existing transformer and approximation methods.
The paper tackles the challenge of modeling long-range dependencies in high-resolution medical images by proposing HCT, an efficient vision model using linear self-attention approximation, which significantly outperforms CNN counterparts on a high-resolution mammography dataset.
Medical images come in high resolutions. A high resolution is vital for finding malignant tissues at an early stage. Yet, this resolution presents a challenge in terms of modeling long range dependencies. Shallow transformers eliminate this problem, but they suffer from quadratic complexity. In this paper, we tackle this complexity by leveraging a linear self-attention approximation. Through this approximation, we propose an efficient vision model called HCT that stands for High resolution Convolutional Transformer. HCT brings transformers' merits to high resolution images at a significantly lower cost. We evaluate HCT using a high resolution mammography dataset. HCT is significantly superior to its CNN counterpart. Furthermore, we demonstrate HCT's fitness for medical images by evaluating its effective receptive field.Code available at https://bit.ly/3ykBhhf