This is the official implementation for article "LANet: Lightweight Attention Network for Medical Image Segmentation".

31.07.2024 - Sign the agreement, the article will be published on Springer

24.04.2024 - Attended the conference, waiting for final publication

11.03.2024 - The article is accepted and will be published after the conference which will be held on Azerbaijan.

20.12.2023 - The article is submitted in Springer proceedings of the ITTA-2024 conference (https://itta.cyber.az).

LANet, a Lightweight Attention Network, are presented in the paper and incorporates an Efficient Fusion Attention (EFA) block and an Adaptive Feature Fusion (AFF) decoding block. The model adopts MobileViT as a lightweight backbone network with a small number of parameters, facilitating easy training and faster predictive inference.

The EFA block enhances the model's feature extraction capability by capturing task-relevant information while reducing redundancy in channel and spatial locations.

The AFF decoding block fuses the purified low-level features from the encoder with the sampled features from the decoder, enhancing the network's understanding and expression of input features.

• torch == 2.1.1+cu121
• tensorboard == 2.11.2
• numpy == 1.24.1
• python == 3.9.18
• torchvision == 0.16.1+cu121
• ...

The efficiency of LANet was evaluated using four public datasets: kvasir-SEG, CVC-clinicDB, CVC-colonDB, and the Data Science Bowl 2018. All datasets used in paper are public, you can download online.

Split the datasets for train, validation and test with ratio 8:1:1

waiting...

❗ 👀 The codes can not be used for commercial purposes!!!