# HLFSRNN-MIL: A Hybrid Multi-Instance Learning Model for 3D CT Image Classification. _Applied_
## 2 Related Works
Through training, a certain number of top instances that contribute the most to the label in the package are selected as the input for subsequent bag prediction. Subsequently, an adaptive bilateral adjacency matrix will be constructed to find vertex relationships at different granularity levels, and a graph inference module (BA-GCN) will be used to predict the patient's diagnostic probability. [PERSON] et al. [27] proposed a multi-instance learning approach based on two-stage attention combined with transfer learning (TSA-MIL), which adjusts the attention weight of each instance by determining the distance between the key instance and the current instance. During the feature space processing, they adopted a clustering constraint method to refine the classification features, thereby enhancing the classification performance of TSA-MIL. [PERSON] et al. [28] proposed a self-tuning module to mitigate the impact of unbalanced label distribution in positive packets. The confidence-based regularization strategy generates pseudo-labels for each weak input through prediction. By incorporating a Student-Teacher architecture, the model can leverage unlabeled data for training, thereby enhancing the model's recognition ability in a cost-effective manner. [PERSON] et al. [29] proposed an adversarial multi-instance learning (AdvMIL) model, which enhances the distribution estimation performance of AdvMIL by employing time-adversarial instances. This approach eliminates the necessity of fully supervised learning for data in the model, effectively reducing the computational cost of AdvMIL and enhancing the robustness of mainstream survival analysis methods. [PERSON] et al. [30] introduced the Attention Gaussian Process (AGP) method in multi-instance learning, which leverages a combination of the Gaussian process and attention mechanism to predict the probability of instance-level labels. The packet-level predictions can be elucidated by the instance-level prediction results. This approach effectively ensures accurate fitting and predictive estimation performance of the model even when the data are unbalanced. [PERSON] et al. [31] proposed an Iteratively Coupled Multi-Instance Learning (ICMIL) framework, which fine-tunes the parameters of the feature-extracted instance embedder using packet-level category information. This framework facilitates information exchange between the low-cost coupled instance embedder and the packet classifier, thus aiding in generating improved instance representations. In addressing the challenge of early clinical diagnosis of low-level characteristic symptoms, [PERSON] et al. [32] introduced a multi-instance learning framework based on a causal-driven graph neural network. This framework encodes spatial proximity and feature similarity between instances while emphasizing the causal components of instance features through a causal comparison mechanism, thus enhancing the diagnostic performance of the model. These methods have played a significant role in advancing multi-instance learning, and a common trend among them is the exploration of more complex feature extraction techniques. Many current methods rely on feature extraction modules based on deep neural networks, such as ResNet [33], MobileNetV3 [34], and GoogleNet [35], among others. These network models have great differences in design concepts and performance. ResNet connects residual blocks through identity mapping to solve the problem of gradient explosion and vanishing in training. Using the inverted residual structure and separable convolution, MobileNetV3 reduces the required computing resources, enhances the feature relationship between channels, and further improves the performance of the model. GoogleNet uses a combination of convolution kernels of different shapes and pooling methods to extract features from multiple scales, which improves the utilization of computing resources. However, lesions in CT images often exhibit complex characteristics such as uncertain location, blurred shape, and varying sizes.