Publications by authors named "Mane Williams"

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features.

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Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale.

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Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale.

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Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation, with concomitant risks of sampling bias and misdiagnosis. To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities.

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The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone and do not address how these data sources can be integrated to develop joint image-omic prognostic models. Additionally, identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest.

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Article Synopsis
  • Endomyocardial biopsy (EMB) is crucial for detecting heart transplant rejections but suffers from inconsistent manual interpretation by pathologists, leading to unnecessary treatments and poor outcomes.
  • A new AI system has been developed to automate the analysis of EMB images, accurately detecting various types and grades of allograft rejection with high performance metrics.
  • In comparison to traditional methods, this AI system not only maintains equivalent accuracy but also reduces variability among different human reviewers, potentially enhancing the effectiveness of heart transplant monitoring.
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  • - Undergraduate students at UCLA conducted research using RNA interference (RNAi) and fluorescent proteins to pinpoint genes crucial for blood cell development in fruit flies, screening around 3,500 genes and finding 137 that affected hematopoiesis.
  • - By targeting RNAi to different cell types involved in blood cell maturation, the researchers identified specific gene subsets that either facilitate or inhibit this process, revealing new insights into gene functions related to RNA processing and vesicular trafficking.
  • - The CURE (Course-Based Undergraduate Research Experience) model not only enhanced students' understanding and skills in science but also improved retention rates in STEM fields, demonstrating the value of hands-on research in education.
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