SAMCNet: Towards a Spatially Explainable AI Approach for Classifying MxIF Oncology Data

Farhadloo, Majid; Molnar, Carl; Luo, Gaoxiang; Li, Yan; Shekhar, Shashi; Maus, Rachel L.G.; Markovic, Svetomir N.; Leontovich, Alexey A.; Moore, Raymond C.

doi:10.1145/3534678.3539168

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…Because immune cells are nurtured by stroma in TIME and play important roles in tumor responses, many studies focus on investigating cell-cell interactions within stroma. [23][24][25] Thus, we compared the detailed stroma cell distribution differences between HQ and LQ groups. Based on our previous quantitative assessment, 69 LQ FOVs (in which N tissue_damage = 26, N halo_artifacts = 45) were identified.…”

Section: Effect Of Poor Qualitymentioning

confidence: 99%

Multiplex Immunofluorescence Image Quality Checking Using DAPI Channel–referenced Evaluation

Jiang

Moore

Jordan

et al. 2023

J Histochem Cytochem.

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Multiplex immunofluorescence (MxIF) images provide detailed information of cell composition and spatial context for biomedical research. However, compromised data quality could lead to research biases. Comprehensive image quality checking (QC) is essential for reliable downstream analysis. As a reliable and specific staining of cell nuclei, 4′,6-diamidino-2-phenylindole (DAPI) signals were used as references for tissue localization and auto-focusing across MxIF staining–scanning–bleaching iterations and could potentially be reused for QC. To confirm the feasibility of using DAPI as QC reference, pixel-level DAPI values were extracted to calculate signal fluctuations and tissue content similarities in staining–scanning–bleaching iterations for identifying quality issues. Concordance between automatic quantification and human experts’ annotations were evaluated on a data set consisting of 348 fields of view (FOVs) with 45 immune and tumor cell markers. Cell distribution differences between subsets of QC-pass vs QC-failed FOVs were compared to investigate the downstream effects. Results showed that 87.3% FOVs with tissue damage and 73.4% of artifacts were identified. QC-failed FOVs showed elevated regional gathering in cellular feature space compared with the QC-pass FOVs. Our results supported that DAPI signals could be used as references for MxIF image QC, and low-quality FOVs identified by our method must be cautiously considered for downstream analyses.

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Section: Effect Of Poor Qualitymentioning

confidence: 99%

Multiplex Immunofluorescence Image Quality Checking Using DAPI Channel–referenced Evaluation

Jiang

Moore

Jordan

et al. 2023

J Histochem Cytochem.

Self Cite

View full text Add to dashboard Cite

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“…In terms of logical-oriented, explanations in healthcare mainly focus on correlation analysis. For example, SHAP has been widely used in the healthcare industry to provide explanations for hospital admission [108], quality of life [109], surgery complication [110], Oncology [111] and risk factor analysis of in-hospital mortality [112].…”

Section: Xai Based Proposalsmentioning

confidence: 99%

Survey on Explainable AI: From Approaches, Limitations and Applications Aspects

Yang

Wei

et al. 2023

Hum-Cent Intell Syst

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In recent years, artificial intelligence (AI) technology has been used in most if not all domains and has greatly benefited our lives. While AI can accurately extract critical features and valuable information from large amounts of data to help people complete tasks faster, there are growing concerns about the non-transparency of AI in the decision-making process. The emergence of explainable AI (XAI) has allowed humans to better understand and control AI systems, which is motivated to provide transparent explanations for the decisions made by AI. This article aims to present a comprehensive overview of recent research on XAI approaches from three well-defined taxonomies. We offer an in-depth analysis and summary of the status and prospects of XAI applications in several key areas where reliable explanations are urgently needed to avoid mistakes in decision-making. We conclude by discussing XAI’s limitations and future research directions.

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“…In the sensitive realm of oncology, the trust in AI systems hinges on their ability to not only perform with high accuracy but also provide clarity on how conclusions are drawn [5]. Traditional neural networks, while proficient in various applications, often operate as "black boxes," offering little insight into their internal decision-making processes [6]. This lack of transparency is particularly problematic in the clinical setting, where understanding the rationale behind diagnostic decisions is imperative for clinician acceptance, quality control, and ethical considerations [7].…”

Section: Introductionmentioning

confidence: 99%

CrossLinkNet: An Explainable and Trustworthy AI Framework for Whole-Slide Images Segmentation

Xiao,

Zhong,

Chen

et al. 2024

CMC

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In the intelligent medical diagnosis area, Artificial Intelligence (AI)'s trustworthiness, reliability, and interpretability are critical, especially in cancer diagnosis. Traditional neural networks, while excellent at processing natural images, often lack interpretability and adaptability when processing high-resolution digital pathological images. This limitation is particularly evident in pathological diagnosis, which is the gold standard of cancer diagnosis and relies on a pathologist's careful examination and analysis of digital pathological slides to identify the features and progression of the disease. Therefore, the integration of interpretable AI into smart medical diagnosis is not only an inevitable technological trend but also a key to improving diagnostic accuracy and reliability. In this paper, we introduce an innovative Multi-Scale Multi-Branch Feature Encoder (MSBE) and present the design of the CrossLinkNet Framework. The MSBE enhances the network's capability for feature extraction by allowing the adjustment of hyperparameters to configure the number of branches and modules. The CrossLinkNet Framework, serving as a versatile image segmentation network architecture, employs cross-layer encoder-decoder connections for multi-level feature fusion, thereby enhancing feature integration and segmentation accuracy. Comprehensive quantitative and qualitative experiments on two datasets demonstrate that CrossLinkNet, equipped with the MSBE encoder, not only achieves accurate segmentation results but is also adaptable to various tumor segmentation tasks and scenarios by replacing different feature encoders. Crucially, CrossLinkNet emphasizes the interpretability of the AI model, a crucial aspect for medical professionals, providing an in-depth understanding of the model's decisions and thereby enhancing trust and reliability in AI-assisted diagnostics.

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SAMCNet: Towards a Spatially Explainable AI Approach for Classifying MxIF Oncology Data

Cited by 7 publications

References 35 publications

Multiplex Immunofluorescence Image Quality Checking Using DAPI Channel–referenced Evaluation

Multiplex Immunofluorescence Image Quality Checking Using DAPI Channel–referenced Evaluation

Survey on Explainable AI: From Approaches, Limitations and Applications Aspects

CrossLinkNet: An Explainable and Trustworthy AI Framework for Whole-Slide Images Segmentation

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