Brief Review — An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning

Conditional GAN (cGAN) + Atrous Convolution (AC) + Channel Attention with Weighting Block (CAW)

Sik-Ho Tsang
4 min readNov 8, 2022

An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning
cGAN+AC+CAW, by Universitat Rovira i Virgili, and Bioinformatics Institute,
2019 arXiv v1 (Sik-Ho Tsang @ Medium)
Medical Image Analysis, Image Segmentation, Image Classification

  • An efficient solution for tumor segmentation and classification in breast ultrasound (BUS) images. An atrous convolution layer is added to the conditional generative adversarial network (cGAN) segmentation model. A channel-wise weighting block is also added.
  • SSIM and L1-norm loss with the typical adversarial loss are proposed as a loss function.


  1. cGAN+AC+CAW for Segmentation
  2. Random Forest for Classification
  3. Results

1. cGAN+AC+CAW for Segmentation

1.1. Generator G

  • The generator network incorporates an encoder section, made of seven convolutional layers (En1 to En7), and a decoder section, made of seven deconvolutional layers (Dn1 to Dn7).
  • An atrous convolution block is inserted between En3 and En4. 1, 6 and 9 dilation rates with kernel size 3×3 and a stride of 2 are used.
  • in addition to a channel attention with channel weighting (CAW) block between En7 and Dn1.
  • The CAW block is an aggregation of a channel attention module (DAN) with channel weighting block (SENet), which increases the representational power of the highest level features of the generator network.

1.2. Discriminator D

  • It is a sequence of convolutional layers.
  • The input of the discriminator is the concatenation of the BUS image and a binary mask marking the tumor area.
  • The output of the discriminator is a 10×10 matrix having values varying from 0.0 (completely fake) to 1.0 (real).

1.3. Loss Function

  • The loss function of the generator G comprises three terms: adversarial loss (binary cross entropy loss), L1-norm to boost the learning process, and SSIM loss to improve the shape of the boundaries of segmented masks:
  • where z is a random variable.
  • The loss function of the discriminator D is:

2. Random Forest for Classification

  • Each BUS image is fed into the trained generative network to obtain the boundary of the tumor, and then 13 statistical features from that boundary are computed: fractal dimension, lacunarity, convex hull, convexity, circularity, area, perimeter, centroid, minor and major axis length, smoothness, Hu moments (6) and central moments (order 3 and below).
  • Exhaustive Feature Selection (EFS) algorithm is used to select the best set of features. The EFS algorithm indicates that the fractal dimension, lacunarity, convex hull, and centroid are the 4 optimal features.
  • The selected features are fed into a Random Forest classifier, which is later trained to discriminate between benign and malignant tumors.

3. Results

3.1. Segmentation

  • This dataset contains 150 malignant and 100 benign tumors contained in BUS images. To train our model, we randomly divided the dataset into the training set (70%), a validation set (10%) and testing set (20%).
Segmentation results

The model (cGAN+AC+CAW) outperforms the rest in all metrics. It achieves Dice and IoU scores of 93.76% and 88.82%, respectively.

Boxplots of IoU and Dice metrics of the proposed model and FCN, SegNet, ERFNet and U-Net.

The proposed model is in the range 88% to 94% for Dice coefficient and 80% to 89% for IoU, while other deep segmentation methods, FCN, SegNet, ERFNet and U-Net show a wider range of values.

Segmentation results on four samples of the BUS dataset.
  • SegNet and ERFNet yield the worst results since there are large false negative areas (in red), as well as some false positive areas (in green).

In turn, U-Net, DCGAN, cGAN provide good segmentation but the proposed model provide more accurate segmentation of the boundary of breast tumors.

3.2. Classification

Breast tumor classification results
  • The proposed breast tumor classification method outperforms [9], with a total accuracy degree of 85%.



Sik-Ho Tsang

PhD, Researcher. I share what I learn. :) Linktree: for Twitter, LinkedIn, etc.