# DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation

arXiv: https://arxiv.org/abs/2112.13559 · HTML: https://mlsanity.com/paper/2112.13559/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 27 Dec 2021.

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Recurrent Models of Visual Attention](https://mlsanity.com/paper/1406.6247/) (2014)
- [SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning](https://mlsanity.com/paper/1611.05594/) (2016)
- [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018)
- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://mlsanity.com/paper/1611.09326/) (2016)
- [HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation](https://mlsanity.com/paper/1804.02967/) (2018)
- [Capsules for Object Segmentation](https://mlsanity.com/paper/1804.04241/) (2018)
- [Automatic segmentation of MR brain images with a convolutional neural network](https://mlsanity.com/paper/1704.03295/) (2017)
- [Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation](https://mlsanity.com/paper/1712.05319/) (2017)
- [Offset Curves Loss for Imbalanced Problem in Medical Segmentation](https://mlsanity.com/paper/2012.02463/) (2020)
- [A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation](https://mlsanity.com/paper/2012.02073/) (2020)
- [Agent-Environment Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/2107.08323/) (2021)
- [Deep Recurrent Level Set for Segmenting Brain Tumors](https://mlsanity.com/paper/1810.04752/) (2018)
- [Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets: Application to IsoIntense Infant Brain MRI Segmentation](https://mlsanity.com/paper/1809.08168/) (2018)

## Built on by (most cited first, 6 of 6)

- [MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation](https://mlsanity.com/paper/2309.03329/) (2023)
- [AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation](https://mlsanity.com/paper/2306.06842/) (2023)
- [EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification](https://mlsanity.com/paper/2210.04615/) (2022)
- [SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Data](https://mlsanity.com/paper/2201.05905/) (2022)
- [FSDENet: A Frequency and Spatial Domains based Detail Enhancement Network for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2510.00059/) (2025)
- [3DConvCaps: 3DUnet with Convolutional Capsule Encoder for Medical Image Segmentation](https://mlsanity.com/paper/2205.09299/) (2022)

## Abstract (arXiv)

While Magnetic Resonance Imaging (MRI) has played an essential role in infant brain analysis, segmenting MRI into a number of tissues such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is crucial and complex due to the extremely low intensity contrast between tissues at around 6-9 months of age as well as amplified noise, myelination, and incomplete volume. In this paper, we tackle those limitations by developing a new deep learning model, named DAM-AL, which contains two main contributions, i.e., dilated attention mechanism and hard-case attention loss. Our DAM-AL network is designed with skip block layers and atrous block convolution. It contains both channel-wise attention at high-level context features and spatial attention at low-level spatial structural features. Our attention loss consists of two terms corresponding to region information and hard samples attention. Our proposed DAM-AL has been evaluated on the infant brain iSeg 2017 dataset and the experiments have been conducted on both validation and testing sets. We have benchmarked DAM-AL on Dice coefficient and ASD metrics and compared it with state-of-the-art methods.