# 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)

--

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**

## 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**comprises*G***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**is:*D*

# 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%)**.

The model (cGAN+AC+CAW) outperforms the rest in all metrics. It achievesDiceandIoUscores of93.76%and88.82%, respectively.

The proposed model is in the range

88% to 94% for Dicecoefficient and80% to 89% for IoU, while other deep segmentation methods, FCN, SegNet, ERFNet and U-Net show a wider range of values.

**SegNet****ERFNet****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

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

## Reference

[2019 arXiv v1] [cGAN+AC+CAW]

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

## Biomedical Multi-Task Learning

**2018 **[ResNet+Mask R-CNN] [cU-Net+PE] [Multi-Task Deep U-Net] [cGAN-AutoEnc & cGAN-Unet] **2019 **[cGAN+AC+CAW]