# Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme
###### Abstract  
Intelligent lung nodules classification is a meaningful and challenging research topic for early precaution of lung cancers, which aims to diagnose the malignancy of candidate nodules from the pulmonary computed tomography images. Nowadays, deep learning methods have made significant achievements in the medical field and promoted developments of lung nodules classification. Nevertheless, mainstream CNNs-based networks typically excel in learning coarse-grained local feature representations via stacked local-aware and weight-shared convolutions, and cannot practically model the long-range context interaction and the spatial dependencies. To tackle the above difficulties, we innovatively propose an effective Multi-Granularity Dilated Transformer to learn the long-range context relations, and explore fine-grained local details via the proposed Local Focus Scheme. Specifically, we delicately design a novel Deformable Dilated Transformer to incorporate diverse contextual information with self-attention for learning long-range global spatial dependencies. Moreover, numerous investigations indicate that local details are extremely crucial to classify indistinguishable lung nodules. Thus, we propose the Local Focus Scheme to focus on the more discriminative local features by modeling channel-wise grouped topology. Consequently, the Multi-Granularity Dilated Transformer is constructed by leveraging the Local Focus Scheme to guide the Deformable Dilated Transformer for learning fine-grained local cues. Experimental results on the mainstream benchmark LIDC-IDRI demonstrate the superiority of our model compared with the state-of-the-art methods. lung nodule classification; dilated transformer; multi-granularity; local focus scheme +
Footnote †: journal: Medical Image and Image Processing  
distribution of all the nodules in the LIDC-LDRI dataset, the malignant nodules in the dataset have diameters that typically exceed 12 mm, and the benign nodules have diameters that are generally less than 5 mm. Early approaches seek to capture salient information from the whole region in the CT image and obtain a global feature representation of the lung nodules. However, partial representation error may lead to a breakdown in exploring holistic structures and notably limits the performance of global features. Furthermore, the global representation generally ignores useful local detail, which also results in relatively poor performance in lung nodule classification, thus recent works commonly regard this nature as multi-scale formulations in the field of computer vision, using patches of multiple sizes [6] to learn scale-invariant representation. However, multi-stage downsample may still discard many contextual features. As emphasized in the above analysis, part-based representations are very crucial to distinguish the category of lung nodules, which can guide the network to learn fine-grained local features. Unlike previous CNNs-based methods, which cannot practically model the long-range context interaction and the global spatial dependencies, we propose a multi-granularity dilated transformer (MGDFormer) to learn robust long-range global representations of various nodules, and explore the discriminative part-based representations to learn fine-grained local features simultaneously. Specifically, we aim to leverage Multi-Head Self-Attention in Transformer to capture global dependencies, but the regular structure will calculate the relationship of all pixels that easily fall into the overfitting problem. We argue that calculating pixel-wise attention of the objective region can obtain discriminative global features and modify the dilated convolutional layer as the feedforward layer to improve receptive fields. As shown in Figure 1, generalized global-local features can be utilized to classify much larger (\(>\)12 mm) and smaller (\(<\)5 mm) nodules, but it is not effective to distinguish hard samples (i.e., 5-12 mm lung nodules).