Advances and Challenges of Machine Learning in Brain Medical

Imaging Data Analysis

Yinuo Zhang

Beijing Normal University-Hong Kong Baptist University United International College,

No. 2000 Jintong Road, Zhuhai, China

Keywords: Machine Learning, Deep Learning, MRI, Brain Tumor Detection, Brain Tumor Segmentation.

Abstract: In recent years, machine learning has become a critical method for medical imaging data analysis and has

solved many problems in medical imaging. This study focuses on brain medical imaging data analysis, and

summarizes the advances and challenges of machine learning in this field. The methods range from traditional

machine learning models to emerging machine learning models. These approaches have been improved and

solved many problems, such as image quality problem, generalization problem and so on. However, there are

still many challenges for machine learning in the field of brain medical imaging, including data annotation

and model training problem, non-interpretability of the model and Cross-domain and cross-site data

integration problem. These challenges not only affect the depth of research, but also hinder the clinical

translation of new technologies. Therefore, how to overcome these obstacles has become the key to promote

the further development of this field. This study also proposes some possible solutions to these challenges

that could be further explored in the future.

1 INTRODUCTION

Nowadays, brain medical imaging is becoming a

significant field because of the increasing number and

complexity of brain disorders. And the appearance of

machine learning has greatly contributed to the

development of this field. As noted by Shrivastava et

al. in recent studies, including preprocessing,

segmentation, feature extraction, and classification,

are helpful to the development of brain medical

imaging analysis. However, new brain diseases keep

coming up over time, and the existing machine

learning methods also have some challenges to

analysis the brain medical imaging specifically and

accurately.

This review aims to summarize the recent

advances of machine learning in brain medical

imaging and also the challenges faced by this field.

The advances represent the progress of machine

learning in brain medical imaging over the years,

including the improvement of old models and the

proposal of new models. The challenges represent

that there is still room for progress in machine

learning in this field, and there are still areas that need

to be improved and strengthened. This study has

sorted out these advances and challenges, which also

has great implications for future research and point

the way.

The rest of this paper is organised as follows:

Section 2,3,4 reviews the advancements and

discusses the challenges of machine learning in brain

medical imaging analysis, Section 5 summarizes the

advances and challenges and also has a discussion of

future research trends.

2 EFFECTS OF MACHINE

LEARNING IN BRAIN

MEDICAL IMAGING

This section will introduce the effects of machine

learning in the processing tasks of medical images,

which mainly focused on technical aspects.

2.1 Basic Overview

Machine learning and even the deep learning have a

mass of effects in the medical image of the brain, such

as image segmentation, classification, and

reconstruction. Fully convolutional networks (FCNs)

are being found useful. Compared with convolutional

Zhang and Y.

Advances and Challenges of Machine Learning in Brain Medical Imaging Data Analysis.

DOI: 10.5220/0013526400004619

In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning (DAML 2024), pages 483-487

ISBN: 978-989-758-754-2

483

neural networks (CNNs), FCNs return to full-

resolution images, which is better at biomedical

image segmentation.

Previous research by Barragán-Montero and

colleagues highlighted a new concept, adversarial

learning, means that the models are trained in the

presence of adverse samples. Generative adversarial

networks (GANs) have been improved step by step

and is widely used in the realm of medical imaging.

As shown by Barragán-Montero et al., GANs are

mostly used in multi-modality image translation and

data augmentation in synthetic picture synthesis.

There are also many other architectures such as

Support Vector Machine (SVM), Long Short-Term

Memory (LSTM), and U-NET (U-Net: Convolutional

Networks for Biomedical Image Segmentation) etc.,

have also been applied in medical imaging field.

2.2 Deep Learning for MRI

Reconstruction

As noted by Yadav et al. in recent studies, the use of

medical imaging is helpful for the recognition of

brain tumors, which could strengthen patient care and

lessen suffering. This is because magnetic resonance

imaging (MRI) uses no ionizing radiation. As shown

by Yadav et al., MRI could display many tissues in

high resolution and great contrast.

A study has shown that Hue, Saturation, Value

(HSV) Histogram and Gabor Wavelet could improve

the accuracy of the MRI imaging analysis. As shown

in Figure 1, the procedure includes preprocessing,

segmentation and clustering, and others.

Figure 1: The Proposed Work Flow (Yadav, D. C., Sharma,

N., & Kudari, J. M., 2023)

A technology proposed by another research has

also contributed to the improvement of MRI image

technology. Previous research by Zhao and

colleagues highlighted that three schemes make up

the technology: U-Net, modified Akima segmented

cubic Hermite interpolation (MASCHI) scheme, and

parallel semi-connected back-propagation neural

network (SJ-BPNN) scheme, which significantly

increased the image resolution.

2.3 Innovations in Deep Learning

Architecture

Based on the attention mechanism, a study by Yang

and colleagues has presented a multi-offset

reconstruction method (AMO-CEST), which can not

only accelerate Chemical Exchange Saturation

Transfer Magnetic Resonance Imaging (CEST-MRI)

acquisition, but also maintain suitable image quality.

This technology improves the quality of MRI image

technology to some extent.

3 MACHINE LEARNING IN

DISEASES DIAGNOSIS

This section will introduce the practical clinical

application of machine learning, especially the

specific tasks in the diagnosis of brain diseases. This

part mainly takes brain tumor disease as an example.

3.1 Deep Learning in Brain Tumor

Detection

A study by Tang and colleagues has proposed a

methodology based on Residual Network with 18

layers (ResNet18) deep learning architecture. In the

study, the authors evaluate neural network by testing

dataset and found that the accuracy, specificity, and

precision are better than previous models in deep

learning. This result indicates that the ResNet18

model can correctly classify the most of the images,

identify healthy brain images and avoid false

positives. One of the reasons is that the ResNet18

model is trained on a large dataset and then use

transfer learning to fine-tuned on a smaller dataset

(Tang et al., 2023). Additionally, ResNet18 model

have residual blocks, which are helpful to solve

gradient degradation problem. As a result, the

ResNet18 is crucial for the classification of brain

diseases, and then the analysis of medical imaging.

Another research by Zubair Rahman and

colleagues has presented a novel Artificial

Intelligence-driven (AI-driven) methodology based

on Efficient Neural Network B2 (EfficientNetB2)

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deep learning architecture. And the architecture

diagram of the model is shown in Figure 2. The

method could deal with a mass of problems that brain

tumor detection faced, such as noise and changes in

image quality, which is helpful for the detection of

brain tumors from MRI images (Zubair Rahman etal.,

2024). The study was tested on a mass of publicly

available data sets and show high accuracy. This

indicates that AI-driven tumor detection models are

not only innovative in theory, but also have wide

application potential in practice.

Figure 2: The Architectural Diagram of Model (Zubair

Rahman etal., 2024)

3.2 Deep Learning in Brain Tumor

Segmentation

As noted by Dong et al. in recent studies, good image

segmentation methods are crucial for the 3D

geometric modelling while diagnosing and operating.

To divide primary brain tumors from normal brain

tissues, a study proposed a deep learning method

based on a 3D U-net with deep supervision and multi-

scale in continuous experiments and innovations. 3D

U-net is used to process 3D medical image data,

making full use of spatial information in volume data

and preserving detailed features through jump

connections. The integration of deep supervision, that

is, supervised learning at multiple layers of the

network, not just at the final output layer, ensures that

multiple layers can be effectively trained.

Additionally, the multi-scale inputs enable the model

to handle tumor regions of different sizes, thus

improving the model's ability to adjust to intricate

tumor form.The algorithm performs exceptionally

well in terms of segmentation accuracy and

processing speed, according to experimental results.

The model is helpful for the clinical diagnosis.

Research by Taleb and colleagues has developed

five 3D self-supervised methods: 3D Contrastive

Predictive Coding, 3D Rotation prediction, 3D

Jigsaw puzzles, Relative 3D patch location, and 3D

Exemplar networks. Based on experimental results,

3D Self-supervised model performs better than 2D

models, in particular, significantly outperforms

models trained from scratch when using fewer

training samples. With 3D self-supervised pre-

training, the model is able to learn richer contextual

information, which is crucial for medical image

segmentation. And 3D Self-supervised model has the

ability to isolate tumors from MRIs accurately.

Additionally, 3D task for pre-training demonstrates

good cross-domain generalization, especially with

less labelled data. Future expansion could extend this

3D pre-training method to other 3D medical imaging

areas such as Computed Tomography (CT) scans,

Positron Emission Tomography (PET), etc. As a

result, self-supervised pretraining is particularly

suitable for scenarios where medical image data is

abundant but annotation is scarce.

4 MACHINE LEARNING

CHALLENGES IN BRAIN

IMAGING

This section will list some of the challenges of

machine learning in brain image data.

4.1 Data Annotation & Training Issues

As shown by Taleb et al., the scarcity of data and

annotations is a major challenge in model

development and application in the medical imaging

field. Acquiring medical image data is complex,

which require high-cost medical equipment and the

operation of professional personnel. Labelling of

these data consumes a mass of time and requires the

domain experts to participate. The accuracy of

labelling directly affects the performance of the

model. It is possible that transfer learning can be used

to reduce the reliance on large-scale labelled medical

data.

As noted by Liu et al. in recent studies, class

imbalances are also challenges of medical image

analysis. Proposing new loss functions has the

potential to solve this problem.

Additionally, as shown by Li et al., although the

machine learning-based brain image analysis

methods proposed by this study have good

performance, it is still difficult to find a balance

between efficiency and accuracy. The computational

complexity could be reduced and the inference speed

improved while maintaining model performance

through model compression technology.

Advances and Challenges of Machine Learning in Brain Medical Imaging Data Analysis

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4.2 Model Non-Interpretability

As shown by Eder et al., the internal decision-making

process of machine learning cannot be explained at all

when working with complex data, which makes it

difficult for healthcare professionals to trust and

verify Artificial Intelligence (AI) results.

Black box algorithms, for example, whose opacity

leads to a host of problems, including potential bias,

attribution of responsibility, patient autonomy, and

erosion of trust (Durán, J. M., & Jongsma, K. R.,

2021). Computer reliability theory supports the

reliability of algorithms without necessarily requiring

their transparency. However, it is crucial to note that

ethical concerns remain important. The doctors must

take the best care based on trust.

4.3 Cross-Domain & Cross-Site

Integration

When data is collected at multiple sites, differences

between the data can interfere with model training

due to different equipment, experimental conditions,

and participant characteristics (Bostami, B.,

Espinoza, F. A., van der Horn, H. J., Van Der Naalt,

J., Calhoun, V. D., & Vergara, V. M.,2022). The site

effect can reduce the generalization ability of the

model. Harmonization may solve this problem to

some extent, which can standardize the data and

improve the reliability of the model.

Datasets from different domains are difficult to

integrate because they differ in collection methods,

labelling standards, and formats (Said, A., Bayrak, R.,

Derr, T., Shabbir, M., Moyer, D., Chang, C., &

Koutsoukos, X., 2023). And data preprocessing

requirements may be different from domain to

domain. It is possible to solve this problem by

unifying data formats or creating flexible

preprocessing frameworks.

5 CONCLUSIONS

This study has discussed the advances in brain

medical imaging and a mass of innovative methods

and models. Although significant advances have

made in the field of brain medical imaging based on

machine learning, the scarcity of data and annotations,

non-interpretability of the model and the problem of

cross-domain and cross-site data integration limit the

broader application of medical learning in this area.

Future research should develop more generalizable

models and combine with interpretable technology.

By solving these problems, brain medical imaging

analysis will make more contributions to personalized

medical and precision medical.

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