# Advances in Computer-Aided Medical Image Processing
## 2 The Formation of Medical Images
### Dataset  
Equation (1) determines the landing application capability of the model: generalization error, where \(n\) represents the number of training samples, and complexity represents the complexity of the model (i.e., the size of the family of functions). \[\epsilon_{test}\leq\hat{e}_{train}+\sqrt{\frac{complexity}{n}} \tag{1}\]From the 1974 AI winter to today, the widespread application of deep learning has benefited from advances in related fields, including central processing unit (CPU) and graphics processing unit (GPU) advances, the availability of big data, and the formation of the underlying models and frameworks for deep learning. Deep learning neural network training often requires a large amount of medical image data, which can improve the accuracy of the model, prevent overfitting of the model, and increase the generalizability of the model. Only when the test error of the model is small can the model have generalizability and be applied in practice. As seen from Formula (1), the larger the size of the dataset is, the greater is the possibility that the deep learning model can be applied in practice [14]. However, for medical applications, datasets are rare and precious: (1) First, for rare tumors, there are not enough cases, which makes the data source very rare. (2) Second, annotation requires considerable time spent by medical experts, and multiple expert opinions are needed to overcome human error. The lack of qualified experts makes the marked trainable dataset even rarer. (3) Meanwhile, due to the privacy of patients and the confidentiality of hospitals, sharing scarce medical data becomes complicated and difficult. For the above reasons, the dataset possessed by deep learning in this field is very limited [15], which obviously contradicts the requirement of deep learning generalization for massive data. When the number of available samples in the training stage is large, deep learning is very effective. Thus, one of the major challenges in applying deep learning to medical images comes from the limited training samples available to build depth models without suffering from overfitting. In this section, we emphasize the types and features of datasets of some open challenges [16; 17], as well as the data in the literature. Table 1 presents a summary of the MRI dataset, and Table 2 presents a summary of the CT dataset. Researchers can obtain data from open challenges based on location and image features or track the corresponding literature datasets to obtain data with regional characteristics. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Tumor Site** & **Dataset Series Name** & **Dataset Composition** \\ \hline \multirow{4}{*}{Brain} & ADNI & 483 elderly controls, 551 MCI and 437 AD. 300 early MCI and \\  & BRATS & 150 late MCI [19; 20; 21] \\  & ABIDE & 11251 training cases and 219 test cases [22; 23; 24; 25] \\  & [PERSON] & 247 images, including 521 ASD and 593 controls [26] \\ \hline \multirow{2}{*}{Breast} & DDSM & 538 positive samples and 2313 negative samples [28; 29] \\  & [PERSON] & 7909 images, including 2480 benign and 5429 images [30] \\ \hline Liver & ISBI 2019 & 20 volumes of T2-SPIR MRI [25; 31] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MRI dataset for the head; with the development of MRI technology, MRI has become an important tool for the diagnosis of head tumors [18]. For head and neck tumors, an MRI can provide clearer soft tissue imaging, and it is easier to find small tumors below the skull, while a CT easily misses small tumors due to cranial interference.