Assessing the Impact of Attention and Self-Attention Mechanisms on the Classification of Skin Lesions
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- TL;DR
- This paper evaluates how different attention and self-attention mechanisms impact the performance of neural networks when classifying skin lesions.
- Problem
- While attention mechanisms promise performance improvements, choosing the right mechanism and hyper-parameters for a specific problem without guaranteed results remains challenging.
- Method
- The authors study and objectively compare modular attention modules integrated into the ResNet architecture alongside self-attention mechanisms using the Skin Cancer MNIST dataset.
- Results
- Attention modules provided noticeable and statistically significant performance improvements for convolutional neural networks, but these gains were inconsistent across different settings.
- Contributions
- An objective comparison and evaluation of various attention and self-attention mechanisms on the task of skin lesion classification.
- Limitations
- Not specified in the abstract.
- Takeaways
- Self-attention mechanisms demonstrated consistent and significant improvements, achieving top performance even in architectures with fewer parameters.
- Applications
- Computer vision tasks, specifically the classification of skin cancer samples using image datasets.
- Topics
- Attention mechanisms; Self-attention; Convolutional neural networks; Skin lesion classification; ResNet
- For industry
- Healthcare and medical imaging
- Why it matters
- Provides clearer empirical guidance on selecting attention mechanisms for medical image analysis tasks like skin cancer detection.
Abstract
Attention mechanisms have raised significant interest in the research community, since they promise relevant improvements in the performance of neural network architectures. However, in any specific problem, we still lack a principled way to choose specific mechanisms and hyper-parameters that lead to guaranteed improvements. More recently, self-attention has been proposed and widely used in transformer-like architectures, leading to significant breakthroughs in some applications. In this work we focus on two forms of attention mechanisms, attention modules and self-attention. Attention modules are used to reweigh the features of each layer input tensor. Different modules have different ways to perform this reweighting in fully connected or convolutional layers. The attention models studied are completely modular and in this work they will be used with the popular ResNet architecture. Self-attention, originally proposed in the area of natural language processing makes it possible to relate all the items in an input sequence. Self-attention is becoming increasingly popular in computer vision, where it is sometimes combined with convolutional layers, although some recent architectures do away entirely with convolutions. In this work, we study and perform an objective comparison of a number of different attention mechanisms in a specific computer vision task, the classification of samples in the widely used Skin Cancer MNIST dataset. The results show that attention modules do sometimes improve the performance of convolutional neural network architectures, but also that this improvement, although noticeable and statistically significant, is not consistent in different settings. The results obtained with self-attention mechanisms, on the other hand, show consistent and significant improvements, leading to the best results even in architectures with a reduced number of parameters.