# Review — A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

## Semi-Supervised Learning, Improved Result Using Unlabeled Data With Pseudo Labels

In this story, **A New Ensemble Learning Framework for 3D Biomedical Image Segmentation**, (NN-Fit), by University of Notre Dame, is reviewed. In this paper:

**A fully convolutional network based meta-learner**is designed to learn how to improve the results from 2D and 3D models (base-learners).- A new training methods, i.e.
**random fit**and**nearest-neighbor (NN) fit**, are proposed to**minimize the overfitting in meta-learner.**

This is a paper in **2019 AAAI**. (Sik-Ho Tsang @ Medium)

# Outline

**Problems in 3D Biomedical Image Segmentation****Framework Overview****Meta-Learner Training Using Pseudo-Labels****Experimental Results**

# 1. Problems in 3D Biomedical Image Segmentation

- Due to the
**limitations of both GPU memory and computing power**, when designing 2D/3D CNNs for 3D biomedical image segmentation, the trade-off between the**field of view**and**utilization of inter-slice information**in 3D images**remains a major concern**. - In addition, usually
**dataset is small**in 3D biomedical image segmentation which easily lead to**overfitting**.

# 2. Framework Overview

- The proposed approach has two main components, as shown above:

**A group of 2D and 3D base-learners**that are trained to**explore the training data from different geometric perspectives.****An ensemble learning framework**that uses a deep learning based**meta-learner**to**combine the results from the base-learners.**

## 2.1. 2D and 3D Base-Learners

## 2.1.1. 2D Models

- The 2D model basically follows the structure of that in
**Suggestive Annotation (SA)**. - It integrates in recent advances of deep learning network design structures, such as batch normalization, residual networks and bottleneck design.

## 2.1.2. 3D Models

- As for the 3D model,
**DenseVoxNet** - 3D FCN architecture is used to fully incorporate 3D image cues and geometric cues.
- It utilizes the state-of-the-art dense connectivity to accelerate the training process, improve parameters and computational efficiency, and maintain abundant (both low- and high-complexity) features
- It takes advantage of auxiliary side paths for deep supervision to improve the gradient flow.

2D modelscan havelarge fields of view in 2D sliceswhile3D modelscanbetter utilize 3D image informationin a smaller field of view.The

mix of 2D and 3D base-learnerscreates the first level ofdiversity.

- To further boost diversity, multiple 2D views (representations) of the 3D images (e.g.,
*xy*,*xz*, and*yz*views) can be created.

Thus, in the framework,

four base-learners are used:A 3DDenseVoxNetfor utilizing full 3D information;three 2D FCNsfor large fields of view in thexy,xz, andyzplanes.

## 2.2. Meta-Learner

- Given a set of image samples,
*X*={*x*1,*x*2, …,*xn*}, and a set of base-learners,*F*={*f*1,*f*2, …,*fm*},**a pseudo-label set**for each*xi*can be obtained as={f1(*PLi**xi*),*f*2(*xi*), …,*fm*(*xi*)}. - That means the pseudo labels are the output of the base-learners.
- The
**input**of our**meta-learner**includes*H***two parts**:and*xi*, where*S*(*PLi*)is a function of*S**PLi*that forms**a representation of**.*PLi* **Separate encoding blocks**(i.e., DenseBlock 1.1 and DenseBlock 1.2) are used for**extracting information from**, respectively, before the information fusion.*S*(*PLi*) and*xi*- The
**auxiliary loss**in the side path can**improve the gradient flow**within the network. - There are multiple design choices for constructing
*S***averaging**has shown to have slightly better results.

# 3. Meta-Learner Training Using Pseudo-Labels

- Instead of using the manually labelled ground truth to supervise the meta-learner training,
**the pseudo-labels**produced by the base-learners are treated as**ground truth.** - Because there are
**multiple possible targets (pseudo-labels) for the meta-learner to fit**, the meta-learner is**unlikely to overfit**any fixed target. - So, unlabelled data can be used.
- The meta-learner training consists of
**two phases**: (1)**random-fit**, and (2)**nearest-neighbor-fit**.

## 3.1. Random-Fit

- In the first training phase (which aims to train the meta-learner H to reach a near-optimal solution), cross-entropy loss is used:

- where
is the*θH***meta-learner’s model parameters**and*lmce*is a multi-class cross-entropy criterion. - In the SGD-based optimization, for one image sample
*xi*,**the random-fit algorithm randomly chooses a pseudo-label from**(see Algorithm 1).*PLi*and sets it as the current “ground truth” for*xi* - This ensures the supervision signals not to impose any bias towards any base-learner.

## 3.2. **Nearest-Neighbor-Fit (NN-Fit)**

- In the second training phase,
**the meta-learner is aimed to be trained to fit the nearest pseudo-label**, to help the model training process converge.

# 4. Experimental Results

- As unlabeled data can be used for training, there are 3 settings:
**Supervised Learning**,**Semi-Supervised Learning**and**Transductive Setting**.

## 4.1. Supervised Learning (Only Training Data)

**Without using unlabeled data, the proposed meta-learner outperforms the above methods on nearly all the metrics**and has a very high overall score, 0.215 (Proposed) vs -0.161 (DenseVoxNet), -0.036 (tri-planar), and 0.108 (VFN).

- As shown above, 2D and 3D base-learners already achieve better results.
- The proposed
**meta-learner**further**improves the accuracy of the base-learners**, and also achieves a result that is**considerably better than the known state-of-the-art methods**(0.9967 vs. 0.9866).

## 4.2. Semi-Supervised Learning

- The
**training set**of HVSMR 2016 is randomly divided into**two groups**evenly,and*Sa*.*Sb* is*Sa***labeled data**andis*Sb***unlabeled data**.- By leveraging unlabeled images, the proposed approach can improve the model accuracy and generalize well to unseen test data.

## 4.3. Transductive Setting

- The full training data is used to train our base learners, and the
**training and testing data**are used to**train our meta-learner**. - The transductive setting plays
**an important role in many biomedical image segmentation tasks**(e.g., for making biomedical discoveries). For example, after biological experiments are finished, one may have all the raw images available and the sole remaining goal is to train a model to attain the best possible segmentation results. **Improved results**are obtained, which as shown in Tables in 4.1.

## 4.4. Ablation Study

- Different combinations of experimental settings are tried.
- e.g.: Using GT data, or PL data, the use of test data for training (transductive learning), random fit alone or both random and NN fits.
**For transductive learning, S9 using PL data only obtains the best results**, even better than using GT+PL data (S5).- (There are also other findings for this table, if interested, please feel free to read the paper.)

## Reference

[2019 AAAI] [NN-Fit]

A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

## Biomedical Image Segmentation

**2015: **[U-Net]**2016**: [CUMedVision1] [CUMedVision2 / DCAN] [CFS-FCN] [U-Net+ResNet] [MultiChannel] [V-Net] [3D U-Net]

**2017**: [M²FCN] [Suggestive Annotation (SA)] [3D U-Net+ResNet] [Cascaded 3D U-Net] [DenseVoxNet]

**2018**: [QSA+QNT] [Attention U-Net] [RU-Net & R2U-Net] [VoxResNet] [UNet++] [H-DenseUNet]

**2019**: [DUNet] [NN-Fit]

**2020**: [MultiResUNet] [UNet 3+] [VGGNet for COVID-19] [Dense-Gated U-Net (DGNet)]