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How I failed machine learning in medical imaging - shortcomings and recommendations

By Gaël Varoquaux, Veronika Cheplygina

Abstract:
Medical imaging is an important research field with many opportunities for improving patients' health. However, there are a number of challenges that are slowing down the progress of the field as a whole, such optimizing for publication. In this paper we reviewed several problems related to choosing datasets, methods, evaluation metrics, and publication strategies. With a review of literature and our own analysis, we show that at every step, potential biases can creep in. On a positive note, we also see that initiatives to counteract these problems are already being started. Finally we provide a broad range of recommendations on how to further these address problems in the future. For reproducibility, data and code for our analyses are available on https://github.com/GaelVaroquaux/ml_med_imaging_failures

Introduction:

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The field of medical imaging has developed rapidly in the past several decades, with a number of promising applications in medical diagnosis, screening, treatment, and prognosis.

However, there are a number of problems slowing down the progress and hampering its impact on the general public. In this paper, we will look at some of these problems. We will start with a look at the challenges of choosing datasets, methods, metrics, and publication strategies. We will then look at how these choices can be affected by the biases of researchers and how they can be used in people's favor. The last part will be dedicated to the future of the field and how to be more efficient at addressing these challenges in the future.

The rest of this paper is structured as follows:

We start with a brief overview of the main medical imaging and the current state of the art in section 2. In section 3, we will focus on the problems of choosing datasets, methods, evaluation metrics, and publication strategies. In section 4, we will start looking at the biases that can creep in at these stages. In section 5, we will give a few examples of how these biases can be used to one's advantage. In section 6, we will focus on the future of medical imaging, and the potential ways to be more efficient.

2. A brief overview of medical imaging and the current state of the art

Medical imaging is the process of acquiring and interpreting data from images of the body. The most common imaging modality is still X-Rays, which provide a 2D projection of 3D data. The next most common is Magnetic Resonance Imaging (MRI), which gives a 3D projection of the body. However, it is less commonly used in practice. Other modalities include Computed Tomography (CT) and ultrasound (US). X-Rays and CT are used for imaging the internal organs, while MRI and US are used for imaging the body's soft tissues.

2.1. Modality-specific challenges:

With a further breakdown, imaging modalities have their own quirks. X-Rays, for example, are prone to image noise, which is a problem that is being addressed by researchers. CT and US, on the other hand, are prone to artifacts that can distort the image.

2.2. Modality-independent challenges:

Regardless of the modality used, the most common challenges in medical imaging are related to quantifying the tissue. The most popular metrics are the Hounsfield unit, which is a scalar value that indicates the radiodensity of the tissue, and the texture-based DICE coefficient, a scalar value that indicates the texture complexity of the tissue.

However, the Hounsfield unit is prone to certain biases, such as the fact that it does not take into account the amount of calcium in the tissue. The DICE coefficient, on the other hand, is susceptible to differences in the shape of the tissue. For example, it is easier to obtain high DICE coefficients from heart muscle as opposed to heart fat.

2.3. Current state of the art:

While the field of medical imaging is becoming more widespread, the process of quantifying the tissue remains difficult and imprecise. There are a large number of methods that have been proposed, but the most widely used methods are still the Hounsfield unit and the DICE coefficient.

3. Problems with choosing datasets, methods, evaluation metrics, and publication strategies

The problems of choosing datasets, methods, evaluation metrics, and publication strategies can be addressed by looking at how they impact the biases of researchers. In section 3.1, we will look at how researchers use different datasets. In section 3.2, we will look at how researchers use different methods. In section 3.3, we will look at how researchers use different evaluation metrics. In section 3.4, we will look at how researchers use different publication strategies. In section 3.5, we will summarize the different biases that can be seen at these different stages.

3.1. Datasets and biases:

The most commonly used datasets are the synthetic datasets, the public datasets, and the customized datasets. In section 3.1.1, we will look at how these different datasets can be biased.

3.1.1. Synthetic datasets:

Synthetic datasets are datasets that are generated to be similar to real world data. However, they are also prone to certain biases:

3.1.1.1. The generation bias:


The generation bias is an issue that is common to many synthetic datasets. Since they are generated to be similar to real world data, they can be prone to similar biases. For example, one of the most popular synthetic datasets is the Digital Imaging and Communications in Medicine (DICOM) dataset, which is generated from CT scan data. However, since CT scans are prone to artifacts, the DICOM dataset is also prone to certain artifacts that can give rise to certain biases.

3.1.1.2. The dataset bias:

Another issue that is common to synthetic datasets is that they are designed to be similar to real world data, but that does not mean that they are representative of it. For example, the DICOM datasets are designed to be similar to real world data, but they do not necessarily include all the information that is relevant to real world data.

3.1.1.3. The overfitting bias:

The overfitting bias refers to the fact that synthetic datasets are usually not as "dirty" as real world datasets. For example, synthetic datasets contain data that is usually artificially added to provide some noise, but real world datasets have data that is there naturally, such as motion blur.


3.1.2. Public datasets:

Public datasets are datasets that are made available to the public by organizations such as the NIH. They are usually freely available to the public and can be used by anyone.

3.1.2.1. The availability bias:

The availability bias is a common issue with public datasets. While they are freely available to the public, that does not mean that they are easy to obtain. For example, the NIH provides a large number of public datasets, but they are not easy to obtain, and they are often incomplete.

3.1.2.2. The data bias:

Another issue with public datasets is that they are usually not representative of real world data. This is because they are usually produced by organizations that have some interest in the data. For example, the NIH is interested in medical data, which can lead to certain biases in the data.