  The emergence of deep learning techniques has advanced the image segmentation
task, especially for medical images. Many neural network models have been
introduced in the last decade bringing the automated segmentation accuracy
close to manual segmentation. However, cutting-edge models like
Transformer-based architectures rely on large scale annotated training data,
and are generally designed with densely consecutive layers in the encoder,
decoder, and skip connections resulting in large number of parameters.
Additionally, for better performance, they often be pretrained on a larger
data, thus requiring large memory size and increasing resource expenses. In
this study, we propose a new lightweight but efficient model, namely LiteNeXt,
based on convolutions and mixing modules with simplified decoder, for medical
image segmentation. The model is trained from scratch with small amount of
parameters (0.71M) and Giga Floating Point Operations Per Second (0.42). To
handle boundary fuzzy as well as occlusion or clutter in objects especially in
medical image regions, we propose the Marginal Weight Loss that can help
effectively determine the marginal boundary between object and background.
Furthermore, we propose the Self-embedding Representation Parallel technique,
that can help augment the data in a self-learning manner. Experiments on public
datasets including Data Science Bowls, GlaS, ISIC2018, PH2, and Sunnybrook data
show promising results compared to other state-of-the-art CNN-based and
Transformer-based architectures. Our code will be published at:
https://github.com/tranngocduvnvp/LiteNeXt.
