# HLFSRNN-MIL: A Hybrid Multi-Instance Learning Model for 3D CT Image Classification. _Applied_
## 1 Introduction  
In the description of classical image classification problems based on machine learning, images typically belong to a specific category. However, in some practical scenarios, a label is assigned after observing multiple images. This process is analogous to analyzing a video, where the judgment is made only after a comprehensive view. This scenario aligns with the general concept of multi-instance learning (MIL). In 1997, [PERSON] et al. proposed the concept of multi-instance learning to address the challenge of drug activity prediction. They employed the axis-parallel rectangle (APR) learning algorithm in conjunction with multi-instance learning to tackle this issue. Within the framework of multi-instance learning, a method for handling weakly supervised problems is elucidated. In this process, labels are not assigned to individual samples. Instead, based on the problem's specifications, a subset of instances (samples) meeting the problem's criteria is grouped into a bag, and a label is then assigned to each bag. If at least one positive instance exists within a bag, the label of the bag is defined as positive; otherwise, if all instances are negative, the bag is considered negative [1]. The primary advantage of multi-instance learning lies in its simplification of dataset construction for the respective problem, thereby reducing theworkload for professionals. This advantage is particularly pronounced in medical imaging tasks, such as computer histopathology, X-ray imaging, or CT imaging screening. Given the potential existence of various associations and features among different image data, organizing these related images into bags enables multi-instance learning to better capture this information. Consequently, this approach enhances the performance and accuracy of the model [2]. Currently, numerous medical models rely on multi-instance learning frameworks to predict bag labels. Another crucial task involves assigning prediction weights to the outcomes within the bag. This process helps identify key instances that contribute to the bag label, which holds significant importance in certain medical clinical tasks. It plays a pivotal role in enabling doctors to locate the disease and formulate precise treatment plans accordingly [3]. To address this challenge, researchers have proposed various solutions. One approach involves making judgments based on the similarity at the bag level. This method utilizes the difference vector between the bag and all other bags as the feature representation, followed by prediction using this representation [4]. Another method entails embedding all instances within a bag into a compact representation, subsequently employing a bag-level classifier for prediction [5]. The third approach involves predicting low-dimensional embedded features via an instance-level classifier [6]. Among these methods, only the third can offer interpretable results. However, many researchers note that the instance-level classifier often exhibits lower prediction accuracy compared to the bag-level classifier [7]. Furthermore, in cases where there is a strong correlation among instances, employing this method may lead the model to detect abnormalities in different data segments with minor variations in the training data. Consequently, this can result in discrepancies in disease localization [8]. Computed tomography (CT) scan images currently serve as an effective method for clinically examining various diseases, including lung tumors, pneumonia, and others. According to the American Cancer Society, patients detected with lung cancer at an early stage through CT scan images have a 47% survival rate, while 80% of patients can be accurately diagnosed and treated at the central or advanced stages of cancer. A notable feature of CT scan images is their unobtrusiveness. The scanning process is relatively quiet, and compared to other imaging techniques like parallel imaging, changes in angles seldom draw attention [9]. When X-rays capture parts with a 3D structure, they are projected onto a 2D view.