Review — DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation

DS-TransUNet, Using Dual Swin Transformer + U-Net

Sik-Ho Tsang
7 min readApr 26, 2023

DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation,
DS-TransUNet, by Harbin Institute of Technology, and The Chinese University of Hong Kong,
2022 TIM, Over 100 Citations (

@ Medium)

Biomedical Image Segmentation
2015 … 2022 [UNETR] [Half-UNet] [BUSIS] [RCA-IUNet] 2023 [DCSAU-Net]
==== My Other Paper Readings Are Also Over Here ====

  • The patch division used in the existing Transformer-based models usually ignores the pixel-level intrinsic structural features inside each patch.
  • Dual Swin Transformer U-Net (DS-TransUNet) is proposed, which incorporate the hierarchical Swin Transformer with U-Net.
  • Particularly in encoder, dual-scale encoding is used to extract the coarse and fine-grained feature representations of different semantic scales.
  • Meanwhile, a well-designed Transformer interactive fusion (TIF) module is proposed to effectively perform multiscale information fusion.

Outline

  1. Use of Swin Transformer
  2. DS-TransUNet
  3. Results

1. Use of Swin Transformer

a) Architecture of a standard Transformer block. (b) Schematic of a Swin Transformer block.
  • (a) Conventional ViT: is useful to model the long-range dependency but with quadratic computational complexity.
  • (b) Swin Transformer: constraint the self-attention within a window so as to imitate the complexity issue. Yet, the correlation is removed due to the use of local window.
  • Thus, Swin Transformer uses the shifted window, so that every pixels are having self-attention with cascaded self-attention module.
  • Swin Transformer has different stages, the inputted features will go through patch merging layer to reduce the feature resolution and increase dimension.

This will reduce the number of tokens by 2×, perform 2× downsampling of resolution, and increase the output dimension by 2×. So the output resolutions of four stages are H/s × W/s, H/2s × W/2s, H/4s × W/4s, and H/8s × W/8s, and the dimensions are C, 2C, 4C, and 8C, respectively, where s is the patch size.

In this work, dual Swin Transformers are used at encoding path.

2. DS-TransUNet

Illustration of the Proposed Dual Swin Transformer U-Net (DS-TransUNet).

2.1. Overall Architecture

  • Given an input medical image, it is first split into non-overlapping patches at two scales and fed into the two branches of encoder separately, and then the output feature representations of different scales will be fused by the TIF module.
  • Finally, the fused features are restored to the same resolution as an input image after the up-sampling process based on the Swin Transformer block.
  • As such, the final mask predictions are obtained.

2.2. Dual-Scale Encoding Mechanism

ViT and Swin Transformer treat an image as a sequence of non-overlapping patches, ignoring the pixel-level intrinsic structure features inside each patch, which will lead to loss of shallow features such as edges and lines information.

  • Specifically, two independent branches with patch size of s=4 (primary) and s=8 (complementary) are used for feature extraction at different spatial levels.
  • Specifically, the outputs of two branches are denoted as Fi & Gi.

2.3. Transformer Interactive Fusion (TIF) Module

Illustration of TIF module, which serves as the core component of DS-TransUNet in the multiscale features’ fusion process.
  • Then, the transformed output of Gi is obtained by:
  • where ˆgi has the size of 1×(i×C) and LP(·) stands for the linear projection. Avgpool(·) means the average-pooling layer, followed by the flatten operation.

The token ˆgi represents the global abstract information of Gi to interact with Fi at the pixel level.

  • Specifically, Fi is concatenated with ˆgi into a sequence of 1+h×w tokens, which are fed into the Transformer layer for computing global self-attention.
  • where Fiout as the final output of small-scale branch in TIF.
  • Giout, has the size of (h/2×w/2)×(i×c), is obtained from the large-scale branch. Finally, the output feature representation can be acquired as follows:
  • where Conv3×3(·) is a 3×3 convolution layer and Up(·) means the 2× bilinear up-sampling process.
  • The resulting features Ziout are passed to the decoder via skip connections.

2.4. Decoder

  • Specifically, the output of stage 4 in the encoder is used as the initial input of the decoder.
  • In each stage of the decoder, the input features are up-sampled by 2×.
  • After that, the output is fed into the Swin Transformer block for self-attention computation. There are some advantages of such a design:
  1. It allows to make full use of the features from the encoder and up-sampling, and
  2. it can build long-range dependencies and obtain global context information during the up-sampling process to achieve better decoding performance.
  • There are 3 stages, where each stage will increase the resolution of feature maps by 2× and reduce the output dimension by 2×.
  • Finally, the input image is downsampled by cascading two blocks to get low-level features with resolution of (H/2)×(W/2) and H×W, where each block consists a 3×3 convolutional layer, a group normalization layer, and a ReLU layer successively.
  • At the end, all the features at the end are used to predict the segmentation masks.

2.5. Loss Function

  • The loss function is composed of weighted IoU loss LWIoU and binary cross-entropy loss LWBCE.
  • Inspired by [23], deep supervision helps the model training by additionally supervising the output S2 of stage 4 in the encoder and S3 of stage 1 in the decoder, which means the final loss function Ltotal can be written as:
  • where α, β, and γ are empirically set to 0.6, 0.2, and 0.2, respectively.

3. Results

3.1. Datasets

  • Polyp segmentation task: 5 public endoscopic image datasets.
  • 3 additional medical image segmentation tasks: skin lesion segmentation on ISIC 2018 dataset; 2) gland segmentation on gland segmentation (GLAS) dataset; and 3) nuclei segmentation on 2018 Data Science Bowl (Bowl) dataset.

3.2. Polyp Segmentation

Polyp Segmentation Task (a) Kvasir, (b) CVC-ClinicDB (*: Reimplementation, -: Not Available)

DS-TransUNet-L can achieve the highest scores on almost all evaluation metrics for independent datasets, which indicates that Swin Transformer has tremendous potential to replace the traditional CNNs.

Comparison of qualitative results between DS-TransUNet and the existing models on the polyp segmentation task.

DS-TransUNet produces high-quality segmentation masks on cross-study of the polyp segmentation task.

3.3. Three Additional Segmentation Tasks

ISIC 2018 Dataset

DS-TransUNet consistently outperforms these Transformer-based competitors.

GLAS Dataset

DS-TransUNet still outperforms the previous baselines and yields the highest mDice and mIoU scores of 0.878 and 0.791. DS-TransUNet is clearly superior to the recent Transformer-based work.

2018 Data Science Bowl Dataset

DS-TransUNet achieves the highest scores 0.922 and 0.943 in terms of F1 (mDice) and recall.

Qualitative results of DS-TransUNet on three medical image segmentation tasks compared with other models. (a) ISIC 2018 dataset, (b) GLAS dataset, and (c) 2018 Data Science Bowl dataset, respectively.

(a) Skin Lessons: DS-TransUNet can effectively capture the boundaries of skin lesions and generate better segmentation prediction.

(b) Gland: DS-TransUNet can bring excellent performance to distinguish the gland itself from the surrounding tissue.

(c) Data Science Bowl: DS-TransUNet can concurrently predict the boundaries of dozens of cell nuclei much more accurately than the existing baselines.

3.4. Ablation Study

Ablation Study of DS-TransUNet
  • U-Net is considered as a vanilla baseline.
  • “U w/ TE” denotes the U-shaped model with a standard Transformer-based encoder.
  • “U w/ SE” denotes the U-shaped model with the Swin-Transformer-based encoder.
  • “U w/ SE+SD” represents the U-shaped model with both the Swin-Transformer-based encoder and decoder.
  • “U w/ DSE + SD” is the U-shaped model with the proposed Dual-Swin-Transformer-based encoder and Swin-Transformer-based decoder.

“U w/ DSE + SD + TIF” is the full DS-TransUNet architecture, which yields the best performance.

Model Size, FLOPs, mDice, mIoU Trade Off

Authors claim that DS-TransUNet can not only produce a good complexity parameter trade-off but also achieve the best segmentation performance.

Different Combinations of Patch Sizes in Dual Swin Encoder
  • With patch size of (4, 4), DS-TransUNet struggles to have a satisfactory result due to the lack of complementary feature information.
  • Oversize patch size of the encoder provides inadequate fine-grained features for medical image segmentation, causing the pixel-level accuracy to decrease.

Patch size of (4, 8) obtains best performance with acceptable FLOPs.

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Sik-Ho Tsang

PhD, Researcher. I share what I learn. :) Linktree: https://linktr.ee/shtsang for Twitter, LinkedIn, etc.