Lightweight SDE-Net Fusing Model-Based and Learned Features for Computational Histopathology

• Published in: IEEE journal of selected topics in signal processing pp. 1 - 16
• Main Authors: Bao, Rina, Zhao, Yunxin, Srivastava, Akhil, Frazier, Shellaine, Palaniappan, Kannappan
• Format: Journal Article
• Language: English
• Published: IEEE 27-09-2024
• Subjects: Adaptation models; cell segmentation; Computational modeling; computational pathology; Computer architecture; Convolution neural networks; Data models; Deep learning; engineered features; hand-crafted features; Histopathology; Image segmentation; Knowledge engineering; medical image analysis; Network architecture; nuclei segmentation; stroma microenvironment; Training
• ISSN: 1932-4553; 1941-0484
• DOI: 10.1109/JSTSP.2024.3470312

Model-based deep learning has the potential to significantly reduce the size of deep architectures while matching the competitive performance of much deeper and wider architectures. We demonstrate the advantage of combining model-based handcrafted features with learned features for AI-enabled computational pathology. Digital histopathology with whole slide image analysis using gigapixel-sized images and deep neural networks are being actively investigated for diagnosis and treatment, but require tens to hundreds of millions of learnable parameters (network weights). Additionally, using deep architectures effectively in medical applications, including pathology, has a number of challenges, including limited supervisory manual expert labels, a paucity of training data to cover clinical heterogeneity, many rare disease classes, complex anatomical structures and very large deep network architectures that have difficulty with domain adaptation along with generalization to new patient cohorts. We propose a lightweight squeeze, delineate, and excitation network (SDENet) deep learning architecture for pathology image cell and nuclei segmentation. SDENet is based on a novel hybrid network modular design, composed of a combination of model-based engineered or predefined filters for extracting salient information based on expert medical knowledge, with a learnable stack of convolution filters to capture structural relationships using complementary bottom-up data driven features. The proposed SDENet model-based approach learns rich feature representations of histopathology images, achieving a highly competitive performance on the MoNuSeg dataset for cell and nuclei segmentation with almost 90% fewer learnable parameters, and generalizes better to unseen image datasets, achieving about 20% higher accuracy on TNBC, compared to the widely used UNet architecture.