# Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview Learning for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2311.01023 · HTML: https://mlsanity.com/paper/2311.01023/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 100 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Language-driven Semantic Segmentation](https://mlsanity.com/paper/2201.03546/) (2022)
- [ActionCLIP: A New Paradigm for Video Action Recognition](https://mlsanity.com/paper/2109.08472/) (2021)
- [Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation](https://mlsanity.com/paper/1802.06955/) (2018)
- [MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model](https://mlsanity.com/paper/2211.00611/) (2022)
- [Searching for Activation Functions](https://mlsanity.com/paper/1710.05941/) (2017)
- [AIM: Adapting Image Models for Efficient Video Action Recognition](https://mlsanity.com/paper/2302.03024/) (2023)
- [MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer](https://mlsanity.com/paper/2301.11798/) (2023)
- [Some Improvements on Deep Convolutional Neural Network Based Image Classification](https://mlsanity.com/paper/1312.5402/) (2013)
- [Resolving Class Imbalance in Object Detection with Weighted Cross Entropy Losses](https://mlsanity.com/paper/2006.01413/) (2020)
- [Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation](https://mlsanity.com/paper/2105.09365/) (2021)

## Abstract (arXiv)

The utilisation of deep learning segmentation algorithms that learn complex organs and tissue patterns and extract essential regions of interest from the noisy background to improve the visual ability for medical image diagnosis has achieved impressive results in Medical Image Computing (MIC). This thesis focuses on retinal blood vessel segmentation tasks, providing an extensive literature review of deep learning-based medical image segmentation approaches while comparing the methodologies and empirical performances. The work also examines the limitations of current state-of-the-art methods by pointing out the two significant existing limitations: data size constraints and the dependency on high computational resources. To address such problems, this work proposes a novel efficient, simple multiview learning framework that contrastively learns invariant vessel feature representation by comparing with multiple augmented views by various transformations to overcome data shortage and improve generalisation ability. Moreover, the hybrid network architecture integrates the attention mechanism into a Convolutional Neural Network to further capture complex continuous curvilinear vessel structures. The result demonstrates the proposed method validated on the CHASE-DB1 dataset, attaining the highest F1 score of 83.46% and the highest Intersection over Union (IOU) score of 71.62% with UNet structure, surpassing existing benchmark UNet-based methods by 1.95% and 2.8%, respectively. The combination of the metrics indicates the model detects the vessel object accurately with a highly coincidental location with the ground truth. Moreover, the proposed approach could be trained within 30 minutes by consuming less than 3 GB GPU RAM, and such characteristics support the efficient implementation for real-world applications and deployments.