Learning visual representations of medical images (e.g., X-rays) is core to medical image understanding but its progress has been held back by the scarcity of human annotations. Existing work commonly relies on fine-t...
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Purpose: Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been published, but comparative stu...
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Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and t...
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Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1–10 cells) spots per patient sampled in a honeycomb pattern, co-registered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x–y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural
The rapidly emerging field of deep learning-based computational pathology has demonstrated promise in developing objective prognostic models from histology whole slide images. However, most prognostic models are eithe...
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With the increasing development of human-AI teaming structures within and across geographies, the time is ripe for a continuous and objective look at the predictors, barriers, and facilitators of human-AI scientific c...
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ISBN:
(纸本)9781665445993
With the increasing development of human-AI teaming structures within and across geographies, the time is ripe for a continuous and objective look at the predictors, barriers, and facilitators of human-AI scientific collaboration from a multidisciplinary point of view. This paper aims at contributing to this end by exploiting a set of factors affecting attitudes towards the adoption of human-AI interaction into scientific work settings. In particular, we are interested in identifying the determinants of trust and acceptability when considering the combination of hybrid human-AI approaches for improving research practices. This includes the way as researchers assume human-centered artificial intelligence (AI) and crowdsourcing as valid mechanisms for aiding their tasks. Through the lens of a unified theory of acceptance and use of technology (UTAUT) combined with an extended technology acceptance model (TAM), we pursue insights on the perceived usefulness, potential blockers, and adoption drivers that may be representative of the intention to use hybrid intelligence systems as a way of unveiling unknown patterns from large amounts of data and thus enabling novel scientific discoveries.
Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic ...
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Solid tumors present a formidable challenge in oncology, necessitating innovative approaches to improve therapeutic outcomes. Proteoglycans, multifaceted molecules within the tumor microenvironment, have garnered atte...
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Solid tumors present a formidable challenge in oncology, necessitating innovative approaches to improve therapeutic outcomes. Proteoglycans, multifaceted molecules within the tumor microenvironment, have garnered attention due to their diverse roles in cancer progression. Their unique ability to interact with specific membrane receptors, growth factors, and cytokines provides a promising avenue for the development of recombinant proteoglycan-based therapies that could enhance the precision and efficacy of cancer treatment. In this study, we performed a comprehensive analysis of the proteoglycan gene landscape in human breast carcinomas. Leveraging the available wealth of genomic and clinical data regarding gene expression in breast carcinoma and using a machine learning model, we identified a unique gene expression signature composed of five proteoglycans differentially modulated in the tumor tissue: Syndecan-1 and asporin (upregulated) and decorin, PRELP and podocan (downregulated). Additional query of the breast carcinoma data revealed that serglycin, previously shown to be increased in breast carcinoma patients and mouse models and to correlate with a poor prognosis, was indeed decreased in the vast majority of breast cancer patients and its levels inversely correlated with tumor progression and invasion. This proteoglycan gene signature could provide novel diagnostic capabilities in breast cancer biology and highlights the need for further utilization of publicly available datasets for the clinical validation of preclinical experimental results.
Introduction: The use of modern technologies has become crucial for enhancing people's awareness of health problems. By early health risk detection, there will be better treatment outcomes, the severity of illness...
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This paper provides information about open source softwares that most often used as a tool to analyze data generated from the Liquid chromatography-mass spectrometer (LC-MS) instrument and including a little discussio...
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