# Chromosome Cluster Type Identification Using a Swin Transformer
## 2 Related Work  
The automated analysis of chromosome images has intrigued researchers for a long time [19, 20, 21, 22, 23, 24]. The earliest methods for chromosome cluster identification exploited geometric features [25, 26, 27]. [PERSON] et al. [25] proposed a geometric method that exploits cut-line to segment touching and partially overlapping chromosomes. [PERSON] and [PERSON] [26] utilized geometric features such as intersection points, as well as endpoints corresponding to the image skeleton of the given chromosome image obtained after preprocessing. [PERSON] and [PERSON] [27] exploited the geometric features of an object, such as its circularity, area, and length. Recently, learning-based methods have been exploited for the analysis  
Figure 1: Flowchart depicting the pipeline of an automated karyotype analysis model. The model consists of _chromosome segmentation_ and _chromosome classification_ stage. The contribution of this paper lies in proposing a _cluster type identification_ module (marked in red) that helps to perform the required chromosome segmentation step.  
of chromosome images [8; 10; 11; 22; 28; 29; 30]. [PERSON] et al. [11] compared the performance of classical machine learning and deep learning methods on the chromosome cluster type identification task. One of the most recent methods proposed for automated cluster type identification in chromosome images is the ResNeXt-WSL [10] model.  
A promising direction for the segmentation of chromosome instances is the use of _co-saliency detection_. The process of locating and highlighting similar salient regions in an image is known as co-saliency detection. It is a useful technique to identify a group of related image regions [31]. The different research directions explored for co-saliency detection can be broadly categorized as optimization-based, graph-based, and deep-learning-based techniques. When posed as an optimization problem, sparse coding [32] and matrix factorization [33] have been used for co-saliency detection. Graph-based co-saliency detection techniques exploit graphs to represent the relationship between different image regions and identify the related image regions [34; 35]. On the other hand, deep-learning-based techniques learn to predict the co-saliency maps by employing deep neural networks [36; 37]. For this research, we keep our focus limited to the classification of chromosome images and overcome the inability of state-of-the-art [10] approaches to understand long-range dependencies and the global context by introducing a transformer-based classification model.  
We hypothesize that due to high inter-class similarity, global context is crucial to correctly identify cluster types in the chromosome images as some classes, for instance, touching chromosome cluster and touching-overlapping chromosome cluster images. However, we observe that all the deep models studied so far for chromosome cluster identification are CNN-based models, and so suffer from the inductive bias observed in CNNs and, more importantly, fail to capture long-range dependencies and global context. We address this limitation of state-of-the-art cluster-type identification methods by proposing a self-attention-based hierarchical model that captures the global context in a chromosome image and thereby allows better performance compared to the state of the art.