KEYWORDS: Image segmentation, Transformers, Kidney, Performance modeling, Education and training, Visual process modeling, Deep learning, Data modeling, Tissues, Pathology
The segmentation of kidney layer structures, including cortex, outer stripe, inner stripe, and inner medulla within human kidney whole slide images (WSI) plays an essential role in automated image analysis in renal pathology. However, the current manual segmentation process proves labor-intensive and infeasible for handling the extensive digital pathology images encountered at a large scale. In response, the realm of digital renal pathology has seen the emergence of deep learning-based methodologies. However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation. Addressing this gap, this paper assesses the feasibility of performing deep learning based approaches on kidney layer structure segmetnation. This study employs the representative convolutional neural network (CNN) and Transformer segmentation approaches, including Swin-Unet, Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We quantitatively evaluated six prevalent deep learning models on renal cortex layer segmentation using mice kidney WSIs. The empirical results stemming from our approach exhibit compelling advancements, as evidenced by a decent Mean Intersection over Union (mIoU) index. The results demonstrate that Transformer models generally outperform CNN-based models. By enabling a quantitative evaluation of renal cortical structures, deep learning approaches are promising to empower these medical professionals to make more informed kidney layer segmentation.
Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health. The current description and quantification of features on pathology slides are limited, prompting the need for innovative solutions to comprehensively assess diverse phenotypic attributes within Whole Slide Images (WSIs). In particular, understanding the morphological characteristics of podocytes, terminally differentiated glomerular epithelial cells, is crucial for studying glomerular injury. This paper introduces the Spatial Pathomics Toolkit (SPT) and applies it to podocyte pathomics. The SPT consists of three main components: (1) instance object segmentation, enabling precise identification of podocyte nuclei; (2) pathomics feature generation, extracting a comprehensive array of quantitative features from the identified nuclei; and (3) robust statistical analyses, facilitating a comprehensive exploration of spatial relationships between morphological and spatial transcriptomics features. The SPT successfully extracted and analyzed morphological and textural features from podocyte nuclei, revealing a multitude of podocyte morphomic features through statistical analysis. Additionally, we demonstrated the SPT’s ability to unravel spatial information inherent to podocyte distribution, shedding light on spatial patterns associated with glomerular injury. By disseminating the SPT, our goal is to provide the research community with a powerful and user-friendly resource that advances cellular spatial pathomics in renal pathology. The toolkit’s implementation and its complete source code are made openly accessible at the GitHub repository: https://github.com/hrlblab/spatial_pathomics.
Multi-modal learning (e.g., integrating pathological images with genomic features) tends to improve the accuracy of cancer diagnosis and prognosis as compared to learning with a single modality. However, missing data is a common problem in clinical practice, i.e., not every patient has all modalities available. Most of the previous works directly discarded samples with missing modalities, which might lose information in these data and increase the likelihood of overfitting. In this work, we generalize the multi-modal learning in cancer diagnosis with the capacity of dealing with missing data using histological images and genomic data. Our integrated model can utilize all available data from patients with both complete and partial modalities. The experiments on the public TCGA-GBM and TCGA-LGG datasets show that the data with missing modalities can contribute to multi-modal learning, which improvesthe model performance in grade classification of glioma cancer.
KEYWORDS: Data modeling, Performance modeling, Parallel computing, Image analysis, Instrument modeling, Process modeling, Pathology, Neural networks, Data processing, Skin cancer
Contrastive learning, a recent family of self-supervised learning, leverages pathological image analysis by learning from large-scale unannotated data. However, the state-of-the-art contrastive learning methods (e.g., SimCLR, BYOL) are typically limited by the more expensive computational hardware (with large GPU memory) as compared with traditional supervised learning approaches in achieving large training batch size. Fortunately, recent advances in the machine learning community provide multiple approaches to reduce GPU memory usage, such as (1) activation compressed training, (2) In-place activation, and (3) mixed precision training. Yet, such approaches are currently deployed independently without systematical assessments for contrastive learning. In this work, we applied these memory-efficient approaches into a self-supervised framework. The contribution of this paper is three-fold: (1) We combined previously independent GPU memory-efficient methods with self-supervised learning framework; (2) Our experiments are to maximize the memory efficiency via limited computational resources (a single GPU); (3) The self-supervised learning framework with GPU memory-efficient method allows a single GPU to triple the batch size that typically requires three GPUs. From the experimental results, contrastive learning model with larger batch size leads to higher accuracy enabled by GPU memory-efficient method on single GPU.
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