| General note |
Karyotyping continues to be the bedrock of cytogenetic diagnosis, providing key information<br/>on chromosomal abnormalities causative of a broad range of genetic disorders, developmental<br/>abnormalities, and cancers. But standard karyotyping is time-consuming, requires extensive<br/>specialist interpretation, and is vulnerable to human mistake and inefficiency, especially in<br/>high-volume clinical settings. Despite advances in medical image analysis, the automation of<br/>karyotyping faces persistent challenges, including the lack of large-scale annotated datasets,<br/>difficulties in segmenting overlapping chromosomes, and variability in chromosome<br/>morphology and staining. These challenges define a significant research gap in developing<br/>scalable, accurate, and clinically deployable deep learning models for automated chromosome<br/>analysis.<br/>In order to overcome this gap, we first present a large-scale, clinically annotated cytogenetic<br/>database, built from 1,311 patients and consisting of 10,057 karyograms with 514,949<br/>manually annotated chromosome singlets. Furthermore, 3,935 metaphase images are annotated<br/>at the instance level in COCO format. This data set reflects true-world diversity, ranging from<br/>normal and abnormal karyotypes to different Giemsa (G-banding) staining intensities,<br/>structural abnormalities, and overlapping difficult cases, and thus presents a solid basis for the<br/>creation and testing of deep learning models within automated cytogenetics.<br/>Based on this work, we create two primary methodologies aimed at the fundamental tasks of<br/>karyotyping. For segmentation of chromosomes, we introduce a variant Mask R-CNN model<br/>involving an Attention-based Feature Pyramid Network (AttFPN), spatial attention, and a<br/>LastLevelMaxPool component to improve multi-scale feature representation and contextual<br/>perception. It enhances performance in difficult situations, including overlapping<br/>chromosomes and weak banding patterns, and gains considerable improvements in mean<br/>Average Precision (mAP) compared to standard baselines.<br/>For chromosome classification, we present the Dual Attention Multiscale Pyramid Network<br/>(DAMP), a specifically designed model that combines channel and spatial attention<br/>mechanisms to concentrate on discriminative features, as well as a multiscale pyramid<br/>architecture to cope with size, orientation, and quality variation in chromosomes. DAMP's<br/>highest classification accuracy is 96.76% on both public and commercial datasets, performingxv<br/>better than state-of-the-art models like ResNet-50, Vision Transformers, and Siamese<br/>Networks.<br/>Overall, this thesis provides interpretable and scalable deep learning models for automating<br/>chromosome classification and segmentation. Through the closure of key gaps in dataset<br/>quality, model resilience, and clinical utility, the work facilitates the insertion of clever<br/>decision-support systems into cytogenetic pipelines, ultimately leading to improved diagnostic<br/>reliability and efficiency in the face of chromosomal disorders. |
