Multi-Class Cell Detection Using Spatial Context Representation

Unleash the Power of State Space Model for Whole Slide Image With Local Aware Scanning and Importance Resampling

Whole slide image (WSI) analysis is gaining prominence within the medical imaging field. However, previous methods often fall short of efficiently processing entire WSIs due to their gigapixel size. Inspired by recent developments in state space models, this paper introduces a new Pathology Mamba (PAM) for more accurate and robust WSI analysis. PAM includes three carefully designed components to tackle the challenges of enormous image size, the utilization of local and hierarchical information, and the mismatch between the feature distributions of training and testing during WSI analysis. Specifically, we design a Bi-directional Mamba Encoder to process the extensive patches present in WSIs effectively and efficiently, which can handle large-scale pathological images while achieving high performance and accuracy. To further harness the local information and inherent hierarchical structure of WSI, we introduce a novel Local-aware Scanning module, which employs a local-aware mechanism alongside hierarchical scanning to adeptly capture both the local information and the overarching structure within WSIs. Moreover, to alleviate the patch feature distribution misalignment between training and testing, we propose a Test-time Importance Resampling module to conduct testing patch resampling to ensure consistency of feature distribution between the training and testing phases, and thus enhance model prediction. Extensive evaluation on nine WSI datasets with cancer subtyping and survival prediction tasks demonstrates that PAM outperforms current state-of-the-art methods and also its enhanced capability in modeling discriminative areas within WSIs. The source code is available at https://github.com/HKU-MedAI/PAM.

Quantitative Modeling to Characterize Maternal Inflammatory Response of Histologic Chorioamnionitis in Placental Membranes

PROBLEM

The placental membranes are a key barrier to fetal and uterine infection. Inflammation of the membranes, diagnosed as maternal inflammatory response (MIR) or alternatively as acute chorioamnionitis, is associated with adverse maternal-fetal outcomes. MIR is staged 1-3, with higher stages indicating more hazardous inflammation. However, the diagnosis relies upon subjective evaluation and has not been deeply characterized. The goal of this work is to develop a cell classifier for eight placental membrane cells and quantitatively characterize MIR1-2.

METHOD OF STUDY

Hematoxylin and eosin (H&E)-stained placental membrane slides were digitized. A convolutional neural network was trained on a dataset of hand-annotated and machine learning-identified cells. Overall cell class-level metrics were calculated. The model was applied to 20 control, 20 MIR1, and 23 MIR2 placental membrane cases. MIR cell composition and neutrophil distribution were assessed via density and Ripley's cross K-function. Clinical data were compared to neutrophil density and distribution.

RESULTS

The classification model achieved a test-set accuracy of 0.845, with high precision and recall for amniocytes, decidual cells, endothelial cells, and trophoblasts. Using this model to classify 53 073 cells from healthy and MIR1-2 placental membranes, we found that (1) MIR1-2 have higher neutrophil density and fewer decidual cells and trophoblasts, (2) Neutrophils colocalize heavily around decidual cells in healthy placental membranes and around trophoblasts in MIR1, (3) Neutrophil density impacts distribution in MIR, and (4) Neutrophil metrics correlate with features of clinical chorioamnionitis.

CONCLUSIONS

This paper introduces cell classification into the placental membranes and quantifies cell composition and neutrophil spatial distributions in MIR.

Structure Embedded Nucleus Classification for Histopathology Images

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the previous methods. Code and data are made available via https://github.com/lhaof/SENC.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

Predicting the Cell-level PD-L1 Expression Status from H&E-stained Histopathological Slide

TPS (Tumor Proportion Score) serves as a crucial indicator to determine the eligibility of cancer patients for PD-L1 immunohistochemistry (IHC) treatment. In clinical practice, pathologists assess TPS primarily using IHC histopathological slides, which is time-consuming and incurs high treatment costs. In this paper, we introduce a novel approach for predicting PD-L1 positive cells using cost-effective and widely accessible Hematoxylin and Eosin (H&E) stained slides. Here, we first extract features of positive cells from PD-L1 slides. Then mapping the position and expression status of these cells onto H&E slide to generate cell-level pseudo-labels. Finally, we develop a deep correlation model that links PD-L1's cellular expression patterns to the morphological features identifiable in H&E stains. This model enables the direct extraction of PD-L1 markers from H&E-stained slides, not only saving pathologists valuable diagnostic time but also avoiding the high costs associated with PD-L1 staining processes. Extensive results highlight the potential of our model as a promising alternative for assessing PD-L1 in cancer.Clinical Relevance-The proposed cell level detection model can achieve high accuracy. This indicates that predicting PD-L1 expression status from H&E is feasible and helps simplify the detection of immunohistochemistry in clinical practice.

Neural Graph Refinement for Robust Recognition of Nuclei Communities in Histopathological Landscape

Accurate classification of nuclei communities is an important step towards timely treating the cancer spread. Graph theory provides an elegant way to represent and analyze nuclei communities within the histopathological landscape in order to perform tissue phenotyping and tumor profiling tasks. Many researchers have worked on recognizing nuclei regions within the histology images in order to grade cancerous progression. However, due to the high structural similarities between nuclei communities, defining a model that can accurately differentiate between nuclei pathological patterns still needs to be solved. To surmount this challenge, we present a novel approach, dubbed neural graph refinement, that enhances the capabilities of existing models to perform nuclei recognition tasks by employing graph representational learning and broadcasting processes. Based on the physical interaction of the nuclei, we first construct a fully connected graph in which nodes represent nuclei and adjacent nodes are connected to each other via an undirected edge. For each edge and node pair, appearance and geometric features are computed and are then utilized for generating the neural graph embeddings. These embeddings are used for diffusing contextual information to the neighboring nodes, all along a path traversing the whole graph to infer global information over an entire nuclei network and predict pathologically meaningful communities. Through rigorous evaluation of the proposed scheme across four public datasets, we showcase that learning such communities through neural graph refinement produces better results that outperform state-of-the-art methods.

Leukotriene B4 receptor 1 (BLT1) does not mediate disease progression in a mouse model of liver fibrosis

MASH is a prevalent liver disease that can progress to fibrosis, cirrhosis, hepatocellular carcinoma (HCC), and ultimately death, but there are no approved therapies. Leukotriene B4 (LTB4) is a potent pro-inflammatory chemoattractant that drives macrophage and neutrophil chemotaxis, and genetic loss or inhibition of its high affinity receptor, leukotriene B4 receptor 1 (BLT1), results in improved insulin sensitivity and decreased hepatic steatosis. To validate the therapeutic efficacy of BLT1 inhibition in an inflammatory and pro-fibrotic mouse model of MASH and fibrosis, mice were challenged with a choline-deficient, L-amino acid defined high fat diet and treated with a BLT1 antagonist at 30 or 90 mg/kg for 8 weeks. Liver function, histology, and gene expression were evaluated at the end of the study. Treatment with the BLT1 antagonist significantly reduced plasma lipids and liver steatosis but had no impact on liver injury biomarkers or histological endpoints such as inflammation, ballooning, or fibrosis compared to control. Artificial intelligence-powered digital pathology analysis revealed a significant reduction in steatosis co-localized fibrosis in livers treated with the BLT1 antagonist. Liver RNA-seq and pathway analyses revealed significant changes in fatty acid, arachidonic acid, and eicosanoid metabolic pathways with BLT1 antagonist treatment, however, these changes were not sufficient to impact inflammation and fibrosis endpoints. Targeting this LTB4-BLT1 axis with a small molecule inhibitor in animal models of chronic liver disease should be considered with caution, and additional studies are warranted to understand the mechanistic nuances of BLT1 inhibition in the context of MASH and liver fibrosis.

Topology-Guided Multi-Class Cell Context Generation for Digital Pathology

In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statistics and topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.

Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images

Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations. Often, this approach directly applies "natural image driven" MIL algorithms which overlook the multi-scale (i.e. pyramidal) nature of WSIs. Off-the-shelf MIL algorithms are typically deployed on a single-scale of WSIs (e.g., 20× magnification), while human pathologists usually aggregate the global and local patterns in a multi-scale manner (e.g., by zooming in and out between different magnifications). In this study, we propose a novel cross-scale attention mechanism to explicitly aggregate inter-scale interactions into a single MIL network for Crohn's Disease (CD), which is a form of inflammatory bowel disease. The contribution of this paper is two-fold: (1) a cross-scale attention mechanism is proposed to aggregate features from different resolutions with multi-scale interaction; and (2) differential multi-scale attention visualizations are generated to localize explainable lesion patterns. By training ~250,000 H&E-stained Ascending Colon (AC) patches from 20 CD patient and 30 healthy control samples at different scales, our approach achieved a superior Area under the Curve (AUC) score of 0.8924 compared with baseline models. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.