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Artificial Intelligence in Adaptive Radiation Therapy
performance on new, unseen data. Thus, in addition to a training set, two other
hold-out sets, namely validation and test sets, are used. While a training set directly
influences the learning process of a model, a validation set is used to evaluate the
performance of the model during the learning, to tune hyperparameters, and select
the best performing model. The test set on the other hand is never disclosed to
the model during the learning process and is only used to evaluate the performance
once the training is finished. In ML it is common to have a small sized dataset. This
might pose a challenge when splitting the data, since the performance estimates
might be misleading due the small size of partitions. Techniques such as k-fold
cross-validation and stratified k-fold cross-validation mitigate issues related to
small or moderate-sized datasets, offering more accurate estimates of generalization error [39].
1.4.3 Evaluation metrics
Once an ML model is developed, its performance needs to be evaluated, using
quantitative measures. The performance of a predictive model is often measured on
a hold-out test set, i.e. a set that has not been exposed to the model during its
training process. This allows informed decisions to be made in comparing different
models and choosing the one that has the best performance for unseen data
(generalization capability).
The confusion matrix provides detailed insights into predictive model performance, which shows correct and incorrect predictions for each class. Figure 1.12(a)
shows a sample confusion matrix for a binary classification problem. Each entry in
the matrix counts the number of correct/incorrect classifications for each class. True
positive (TP) is the number of positive test samples correctly classified as positive.
Figure 1.12. Performance evaluation: (a) a confusion matrix and (b) an ROC curve.
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Artificial Intelligence in Adaptive Radiation Therapy
Likewise, true negative (TN) is the number of negative samples correctly classified as
negative. False positive (FP) corresponds to the number of negative test samples
misclassified as positive, and false negative (FN) is the number of positive samples
misclassified as negative.
A set of performance measures can be defined to aggregate the matrix entries and
provide more focused insight into a model’s performance, especially in cases of
imbalanced distributions. Three of the most common metrics are recall, precision,
and F1 score.
Recall is the ratio of true positive predictions to the total number of actual
positives:
TP
=+R
TP FN
.
1.9()
Recall measures the ability of a model to capture all positive instances. High recall
means that the model has fewer false negatives. In the example provided in
figure 1.13(a) the model has few false negatives (FN = 3) leading to a relatively
high recall (R = 0.84). Note that a model can have a perfect recall (R = 1) by
predicting all samples as positive (i.e. FN = 0).
Precision, on the other, is the ratio of true positive predictions to the total number
of positive predictions:
TP
=+P
TP FP
.
1.10()
Precision measures the accuracy of positive predictions, and a high precision
means that the model has fewer false positives. In figure 1.13(a), while the model
demonstrated high r ecall, it shows a poor precisi on (only slightly better than
random) due to the relatively large number of f alse positives. The emphasis on
precision or recall depends on the domain and the task at hand. F or instance,
when identifying treatment options, it is crucial to have a h igh precision while for
identifying hi gh-risk patients for screening, a high recall is important.
Figure 1.13. Different fitting scenarios: (a) over-fitting, (b) proper fitting, and (c) under-fitting.
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Artificial Intelligence in Adaptive Radiation Therapy
The F1 score combines precision and recall using their harmonic mean:
×+PR
=
F1 2 .
PR
The F1 score provides a balanced measure of a model’s performance by considering
both false positives and false negatives. It is particularly useful in cases with an
imbalanced class distribution or when both types of classification errors have
significant implications. Figure 1.13(a) shows that the model has a moderate F1
due to low precision.
The receiver operating characteristics (ROC) curve is a valuable tool for assessing
a model’s ability to discriminate between classes, visualizing the tradeoff between the
false positive rate (FPR) and true negative rate (TPR) (see figure 1.13(b)). The area
under the curve (AUC) quantifies model performance, with perfect prediction
yielding an AUC of 1 and random prediction an AUC of 0.5. As figure 1.13(b)
shows, the AUC for classifier 1 is bigger than the AUC for classifier 2, suggesting a
better performance for classifier 1.
1.4.4 Overfitting versus under-fitting
The development of an ML model involves striking a balance between complexity
and simplicity. Neglecting this balance may lead to under-fitting or over-fitting.
Under-fitting means the model is too simplistic to capture the underlying patterns in
the training data, e.g. using a linear classifier for linear non-separable data (see
figure 1.13(a)). An indicator of under-fitting is a low performance or high error rate
on the training data, let alone test data. This is because the model fails to grasp even
the most basic patterns present in the training data.
Conversely, over-fitting occurs when the model has a high learning capacity, and
the training data has a relatively simple distribution (see figure 1.13(b)). High
performance (low error) on training data, but a low performance on test data are
indicators of over-fitting. Hyperparameter tuning is a common approach to prevent
over-fitting. In decision trees, for instance, techniques such as pruning, which
involves limiting the growth of the tree by setting a maximum depth or restricting
the minimum number of samples required to split a node can be used [20].
1.11()
1.5 Generative models
Generative models are a class of ML models designed to learn and mimic the
underlying distribution of a given dataset. Unlike discriminative models, which
focus on predicting labels or classifying data (p(y∣x)), generative models aim to
capture the joint probability distribution of the input data and the corresponding
labels (p(x, y)).
The primary purpose of generative models is to generate new data samples that
are similar to the training data. These models can generate new examples from
scratch by sampling from the learned distribution, allowing them to create realistic
data that preserves the statistical properties of the original dataset. Generative
models have various applications, including data augmentation, image and text
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Artificial Intelligence in Adaptive Radiation Therapy
synthesis, anomaly detection, and semi-supervised learning. They are particularly
useful in scenarios where obtaining labeled data is expensive or impractical, as they
can generate synthetic data for training discriminative models. While various deep
generative models have been proposed in the literature, we focus solely on
introducing the two most prominent and recent techniques in this discussion.
1.5.1 Generative adversarial networks
A generative adversarial network (GAN) [12] is a deep generative model where its
primary goal is to mimic the distribution of training data and, consequently,
generate samples drawn from the learned distribution. GAN is a two-player
minimax game involving two opposing models: a generator G and a discriminator
D, as shown in figure 1.14. In this framework, both models are trained simulta-
neously. The discriminator aims to distinguish between samples from the true data
distribution and the generator distribution. In contrast, the generator seeks to
minimize the likelihood of being identified as fake by approximating the data
distribution from a simpler distribution, such as Gaussian or uniform.
During training, the generator tries to mislead the discriminator, while the
discriminator endeavors to maximize the probability of accurately predicting true
labels for both real and generated samples. The competitive dynamic between these
two components encourages continual improvement. The ideal stopping point is
reached when G captures the distribution of the training data (p
g
= p
), and D can
data
no longer differentiate between generated and training data, resulting in a probability close to 0.5 for each sample.
Given G and D as neural networks, the training of GANs is formulated as
follows. The generator G aims to learn the distribution over data x and a prior on the
input noise variable, defined as p
neural network parameterized by θ
function for the discriminator D. Here, p
(z). G(z; θg) is the mapping function, where G is a
z
. Conversely, D(x; θd)defines the mapping
g
and p
g
represent the generated and
data
training data distributions, respectively. The discriminator’s output is a scalar
representing the label of the input data; that is, D(x) indicates the probability of x
belonging to the training data distribution rather than p
. While D maximizes the
g
probability of assigning true labels to both training and generated samples, G
minimizes log(1 − D(G(z))). Thus, D and G engage in a minimax game to optimize
the following objective function:
Figure 1.14. GAN architecture; z represents noise sampled from a Gaussian distribution, G(z) denotes the
generated image from the noise z, and x represents a training sample drawn from the p
(Reproduced with permission from [
42].)
distribution.
data
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)
0
Artificial Intelligence in Adaptive Radiation Therapy
∼= + −
VDG E Dx E DGz, log log log log 1 .
( ) [ ( )] [ ( ( ( )))]
∼∼
xP x zPzz
() ()
data
1.12
()
Given that discrimination is inherently easier than generation, this objective function
might result in a suboptimal generator. Goodfellow et al [12] proposed reformulating equation (1.12) by replacing the minimization of log(1 − D(G(z))) with the
maximization of log(D(G(z))).
This zero-sum game between these two components can lead to rich representations of the training data, which can be further utilized for downstream tasks [40,
41]. While GANs have demonstrated great success in generating high-quality
samples, they have limitations. Due to their adversarial training nature, they are
known for potentially unstable training and limited diversity in generation.
1.5.2 Diffusion models
While existing deep generative models excel at image generation, they encounter
certain challenges. A diffusion model [43], belonging to the class of generative
models, was introduced to address these challenges by generating high-fidelity
images. Diffusion models contain two main processes: forward diffusion process
and reverse diffusion process. They define a chain of diffusion steps to slowly add
random noise to data and then learn to reverse the diffusion process to construct
desired data samples from the noise.
In the forward diffusion process shown in figure 1.15, we slowly and gradually
add Gaussian noise to the input image x
sampling a data point x
from the real data distribution q(x)(x0∼ q(x)) and then
0
add some Gaussian noise with variance β
with distribution
(
−xt 1
through a series of T steps. We start with
0
to x
t
, producing a new latent variable x
t−1
t
1
bb=−
−
t1: 0
1
qx x Nx x I;1 ,
(∣ ) ( )
−−
tt t tt t11
T
qx x qx ,1.13
(∣) () ()
T
where
The data sample
Figure 1.15. Diffusion process: through the forward process, noise is added to a given image x, drawn from q
(x), through T steps.
respectively. Through the reverse process, the given image x is reconstructed through a denoising process from
step T to 0.
andNdenote the identity matrix and Gaussian distribution, respectively.
gradually loses its distinguishable features as the step increases.
x
0
and
x
represent the original image and the image after T steps of adding noise,
x
T
=
∏
=
t
1-25
p
)
Artificial Intelligence in Adaptive Radiation Therapy
Eventually, when
, it becomes equivalent to an isotropic Gaussian
→∞T
distribution.
The reverse diffusion process involves training a neural network to restore the
original data by reversing the noise applied during the forward pass (see figure 1.15).
Estimating q(x
) can be challenging as it can require the entire dataset. To
t−1∣xt
address this, the reparameterization technique is employed, utilizing a parameterized
model p
small β
in the form of a neural network to learn the parameters. For sufficiently
θ
, the distribution becomes Gaussian, allowing the mean and variance to be
t
parameterized easily:
T
=
xpxpxx
() () ( ∣)
TT
qq
px Nx xt xt;,, ,.
() ( ()())
ttt t1
qq
∏
=
t
m=Σ
−
tt0:
1
1
1.14
q−
()
The network is trained to predict the mean and variance for each time step. Here
xt,t()
q
and
Σqxt,t(
define the mean and the covariance matrix, respectively.
There are different varieties of diffusion models such as diffusion probabilistic
models (DPM) [45], noise-conditioned score network [44], and denoising diffusion
probabilistic models (DDPM) [45].
1.5.3 Applications and use cases
Generative models such as GANs, autoencoders, variational autoencoders, and
diffusion models have been used extensively in the literature for the medical domain,
mainly for image generation to mitigate the limited and imbalanced data problems
in developing AI tools for medical problems [42]. Han et al [46] utilized the DCGAN
and Wasserstein GAN (WGAN) [47] for medical image generation. Nie et al [48]
suggested an adversarial model to generate magnetic resonance imaging (MRI)
images from computed tomography (CT) images, whereas recently the authors of
[49] suggested using denoising diffusion models to generate high-quality MRI and
CT. GANs have also been used for synthetic generation in structured data [50].
Aside from image generation, transfer learning and domain adaptation models
using generative models such as GANs have found widespread application in the
medical field. Domain adaptation is a subcategory of transfer learning when we aim
to learn a model from a source data distribution and apply that model to a different
target data distribution. Yaqoub et al [51] proposed using transfer learning in MR
image reconstruction trained with GANs to mitigate the limited data problem in
medical imaging tasks. Recent studies have applied domain adaptation approaches
to address distribution shifts while leveraging existing medical data. Several studies
considered working on MR images as the source domain and CT images as the
target domain using adversarial training to generate synthetic CT images [52],
cardiac structure segmentation [53], and image registration [54].
Generative models have also been used for anomaly detection. Anomaly
detection, also known as out-of-distribution detection, focuses on recognizing
samples that diverge from the rest of the data, signaling variations in measurement,
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Artificial Intelligence in Adaptive Radiation Therapy
experimental errors, or novel occurrences. This proves valuable in uncovering
unknown anomalies in the medical field, where acquiring a suitable annotated
dataset is consistently a challenge. This approach is also relevant in situations where
information about the specific types of anomalies is scarce. The first anomaly
detection model in medical imaging using GAN was proposed to find anomalies in
optical coherence tomography [55]. While GANs were used mostly for detecting
anomalies a few years ago, more recently, diffusion models have become the
dominant approach. Wolleb et al [56] proposed a novel weakly supervised anomaly
detection method based on denoising diffusion models to address the difficulties of
anomaly detection models based on GANs in preserving fine details in the image.
Pinaya et al [57] proposed a method based on diffusion models to detect and segment
anomalies in brain imaging. Aside from images, anomaly detection using generative
models in time-series data also gained attention [58].
In addition to potential applications of generative models, they gained attention
in other medical imaging tasks. Wolterink et al [59] proposed an adversarial model
by training two separate generators with two different losses and combining them to
reduce the noise in low-dose CT images. Image super-resolution using GANs has
also been investigated in medical images [60]. Different generative models have also
been used extensively for image and video segmentation. Wu et al [61] combined
diffusion and transformer models for medical image segmentation. In another study,
a denoising diffusion probabilistic model was integrated into standard U-Net [62]
models for medical image segmentation [63]. Generative models, specifically GANs,
have been used for other modalities such as text. Even though generating discrete
data such as text with GANs is challenging, several studies investigated using them
for text generation purposes [64].
1.6 Ethical consideration and bias
AI has significantly transformed various aspects of our lives, holding great potential
for societal benefits, ranging from healthcare applications to biomedical data
analysis. In healthcare, AI has the capacity to enhance diagnostic accuracy,
personalize treatment plans, improve patient outcomes, and streamline administrative tasks. Despite these positive impacts, there is also the potential for unintended
harm and misuse. In the following, some of the most important of these potential
harms and misuses will be explained.
1.6.1 Transparency and explainability
A computational system is considered transparent when every detail of its operation
is known, whereas we refer to a system as explainable when humans can understand
how it makes decisions. Transparency in a system does not necessarily ensure
explainability. Understanding how a model operates can be particularly challenging,
especially in the case of DL models with many parameters.
While explainability in DL models is often achieved through techniques such as
layer-wise relevance propagation or attention mechanisms [30] to enhance their
interpretability or model-agnostic approaches such as LIME [65], many classical
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Artificial Intelligence in Adaptive Radiation Therapy
ML models, such as decision trees, SVMs, rule-based systems, and symbolic AI,
inherently offer a certain level of explainability
1.6.2 Bias and fairness
Bias typically denotes a statistical deviation from a defined norm. In AI applications, this divergence often arises from illegitimate or unrelated factors influencing
the output. For instance, gender is irrelevant to job performance; therefore, using
gender as a basis for hiring a candidate is considered irrelevant (example: Amazon
recruiting ML tool)
3
. Similarly, race is unrelated to criminality, making it irrelevant
to incorporate race as a feature for predicting recidivism [66]. While bias can take
various forms in AI models, it is mainly influenced by our data selection strategy.
Data selection bias occurs when the training data are incomplete and comprise
only specific distributions, such as race, gender, ethnicity, etc, making algorithmic
bias likely. In a study by Obermeyer et al [67], an algorithm used in healthcare to
identify and assist patients with complex medical needs was investigated. The study
revealed racial bias in the algorithm, as it systematically underestimated the
healthcare needs of black patients compared to white patients. In another study,
commercial gender classification systems were evaluated [68]. The findings showed
that these systems exhibited higher accuracy for lighter-skinned and male faces
compared to darker-skinned and female faces. The reason behind both models’
biases were traced back to the imbalanced dataset used for training, which primarily
featured specific race or gender categories. As a result, the AI model struggled to
accurately classify in the case of underrepresented groups. In addition to data
selection bias, a model’s goals and validation metrics might impose biases on the
model. Each of these biases can negatively impact marginalized and underrepresented groups, resulting in an unfair AI model.
To mitigate data selection bias and ensure that AI models in healthcare are fair
and effective, it is crucial to train them on large and diverse datasets that accurately
represent the patient population. This diversity needs to encompass various social
statuses, minority groups, age groups, genders, races, and other relevant factors. By
training on such datasets, AI models can learn to make decisions that are more
inclusive and representative of the population they are intended to serve. This
approach not only allows to reduce bias but also ensures that the resulting models
are more robust and applicable across different patient demographics.
1.6.3 Data privacy violation
Modern DL methods heavily depend on large crowd-sourced datasets, which may
contain sensitive or private information. Despite efforts to eliminate sensitive details,
the presence of auxiliary knowledge and redundant encodings poses a risk of deanonymizing datasets. Therefore, prioritizing a privacy-centric design is essential to
safeguard individuals’ information, especially in applying DL techniques to critical
3
https://www.ml.cmu.edu/news/news-archive/2016–2020/2018/october/amazon-scraps-secret-artificial-intel-
ligence-recruiting-engine-that-showed-biases-against-women.html
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Artificial Intelligence in Adaptive Radiation Therapy
domains such as healthcare. Employing methods such as differential privacy and
semantic security ensures data security throughout the model training process [69].
1.6.4 Risk and misuse
The aforementioned issues are primarily linked to poorly defined objectives and
informational imbalances. However, even in cases where a system operates
correctly, it can cause unethical conduct or deliberate misuse [70]. AI models trained
on medical records might memorize patients’ personal information. This means that
the AI could potentially recreate a patient’s data, raising privacy issues and making
it difficult to ensure anonymity when using this data for research or development. In
the worst-case scenario, AI memorizing patient data could be exploited by malicious
actors, leading to situations where sensitive details are used for blackmail or other
criminal purposes. Such models may also be misused by insurance companies to
deny coverage or raise premiums based on pre-existing conditions gleaned from the
memorized data. This could disproportionately harm patients with sensitive health
histories.
In healthcare, the misuse of AI models can also have fatal consequences, such as
intentionally manipulated AI algorithms used for medical diagnosis or treatment
planning, leading to incorrect or harmful recommendations and patient outcomes
[71]. Given the potential risks and misuse of AI applications, regulating these models
seems necessary [72]. AI regulation refers to the establishment and enforcement of
rules, standards, and guidelines governing the development, deployment, and use of
AI systems. These regulations help to (i) ensure that AI systems are developed and
used ethically and responsibly, (ii) safeguard individuals’ privacy such as patients’
data, and (iii) establish accountability and liability in cases of AI-related incidents or
harm.
1.7 Summary
This chapter provided a comprehensive overview of the foundational concepts of AI,
focusing particularly on machine learning and neural networks. It covered the basics
of machine learning, discussing various learning paradigms, such as supervised and
unsupervised learning. The importance of feature engineering, linear separability,
and classical models was emphasized, providing readers with a good understanding
of the essential components of machine learning.
The chapter also explored artificial neural networks, detailing the structure and
function of different types of neural nets, including feed-forward, recurrent,
convolutional networks, and transformers. Attention mechanisms and the training
process for these networks were also covered. Applications and use cases of deep
learning were highlighted, illustrating the practical impact of these technologies.
Additionally, the chapter addressed model training and evaluation, discussing
hyperparameters, data splits, evaluation metrics, and the common challenges of
over-fitting and under-fitting. Finally, we touched on advanced topics such as
generative models and their applications, and concluded with a critical examination
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Artificial Intelligence in Adaptive Radiation Therapy
of ethical considerations in AI, such as transparency, bias, data privacy, and the
risks of misuse.
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