SupCAM: Chromosome cluster types identification using supervised contrastive learning with category-variant augmentation and self-margin loss

Abstract

Chromosome segmentation is a crucial analyzing task in karyotyping, a technique used in experiments to discover chromosomal abnormalities. Chromosomes often touch and occlude with each other in images, forming various chromosome clusters. The majority of chromosome segmentation methods only work on a single type of chromosome cluster. Therefore, the pre-task of chromosome segmentation, the identification of chromosome cluster types, requires more focus. Unfortunately, the previous method used for this task is limited by the small-scale chromosome cluster dataset, ChrCluster, and needs the help of large-scale natural image datasets, such as ImageNet. We realized that semantic differences between chromosomes and natural objects should not be ignored, and thus developed a novel two-step method called SupCAM, which could avoid overfitting only using ChrCluster and achieve a better performance. In the first step, we pre-trained the backbone network on ChrCluster following the supervised contrastive learning framework. We introduced two improvements to the model. One is called the category-variant image composition method, which augments samples by synthesizing valid images and proper labels. The other introduces angular margin into large-scale instance contrastive loss, namely self-margin loss, to increase the intraclass consistency and decrease interclass similarity. In the second step, we fine-tuned the network and obtained the final classification model. We validated the effectiveness of modules through massive ablation studies. Finally, SupCAM achieved an accuracy of 94.99% with the ChrCluster dataset, which outperformed the method used previously for this task. In summary, SupCAM significantly supports the chromosome cluster type identification task to achieve better automatic chromosome segmentation.

Chromosome Cluster Type Identification Using a Swin Transformer

“…A 5-fold cross-validation is performed only for the proposed CCT and the ResNeXt-WSL [10], and the average value of the results obtained for different folds is taken to represent the results of the corresponding method. For the rest of the baselines, due to the limited availability of computational resources, the numbers reported in [10,39] are taken. For all the baselines, pre-trained models trained on ImageNet dataset are used.…”

Automatic detection of translocation t(9;22) using Siamese architecture

“…• Chromosome segmentation: Separating individual chromosomes from the karyogram image. [2][3][4] • Chromosome classification: Accurately identifying specific chromosome types. [5][6][7] • Number abnormality detection: Identifying an extra copy of a chromosome.…”