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Open Access Publications from the University of California

Scholarly Works (6 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2021)

Gleason grading system serves as an essential component in risk stratification and treatment planning for prostate cancer patients. Currently, Gleason grading relies on pathologists to examine glass tissue slides at scanning magnification and localize suspicious regions for higher power examination. Such process can be time-consuming and prone to inter- and intra- observer variability. Moreover, the Gleason grading system may be constrained by its categorization system, which cannot fully capture the disease's continuous feature spectrum. With the recent development of digital slide scanners and the approval of using digitized slides for primary diagnosis by the Food and Drug Administrations (FDA), large numbers of traditional glass slides have been digitized into high resolution whole slide images (WSIs), opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement.

This research attempts to address several challenges in WSI analysis and assist the histopathologic evaluation of prostate cancer. A tile-level semantic segmentation model, which explicitly leverages multi-scale representations, is first proposed to generate pixel-wise Gleason pattern predictions and facilitate estimation of percentage of different patterns. An expectation-maximization (EM)-based semi-supervised learning framework is then developed to exploit information embedded in weakly-labeled samples to further improve the performance of segmentation models, which alleviates the need of expensive pixel-wise annotations. Besides building segmentation tools for tiles extracted from WSIs, a novel multi-resolution multiple instance learning-based model, which can be trained with slide-level labels, is proposed to identify informative regions and provide slide-level Gleason grade group prediction. The model is developed and validated on a large-scale prostate biopsy dataset. Furthermore, a deep learning system, which leverages histopathological features and attention-based aggregation, is built to facilitate predictions of progression-free survival after radical prostatectomy. Together, these models demonstrate the potential of several computer aided diagnosis tools, and pave the road for utilizing computational approaches to optimize and improve the efficiency of prostate cancer diagnosis and risk stratification.

    Cover page: Large-scale Whole Slide Image Analysis with Deep Learning Methods to Improve Prostate Cancer Diagnosis
    • Article
    • Peer Reviewed
    UCLA Previously Published Works (2021)
    Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra-observer agreement. Most previous work on whole slide image analysis has focused on classification or segmentation of small pre-selected regions-of-interest, which requires fine-grained annotation and is non-trivial to extend for large-scale whole slide analysis. In this paper, we proposed a multi-resolution multiple instance learning model that leverages saliency maps to detect suspicious regions for fine-grained grade prediction. Instead of relying on expensive region- or pixel-level annotations, our model can be trained end-to-end with only slide-level labels. The model is developed on a large-scale prostate biopsy dataset containing 20,229 slides from 830 patients. The model achieved 92.7% accuracy, 81.8% Cohen's Kappa for benign, low grade (i.e. Grade group 1) and high grade (i.e. Grade group ≥ 2) prediction, an area under the receiver operating characteristic curve (AUROC) of 98.2% and an average precision (AP) of 97.4% for differentiating malignant and benign slides. The model obtained an AUROC of 99.4% and an AP of 99.8% for cancer detection on an external dataset.
      Cover page: A multi-resolution model for histopathology image classification and localization with multiple instance learning
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2017)
      Gleason grading of histological images is important in risk assessment and treatment planning for prostate cancer patients. Much research has been done in classifying small homogeneous cancer regions within histological images. However, semi-supervised methods published to date depend on pre-selected regions and cannot be easily extended to an image of heterogeneous tissue composition. In this paper, we propose a multi-scale U-Net model to classify images at the pixel-level using 224 histological image tiles from radical prostatectomies of 20 patients. Our model was evaluated by a patient-based 10-fold cross validation, and achieved a mean Jaccard index of 65.8% across 4 classes (stroma, Gleason 3, Gleason 4 and benign glands), and 75.5% for 3 classes (stroma, benign glands, prostate cancer), outperforming other methods.
        Cover page: A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies.
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2019)
        Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network framework for multi-task prediction using an epithelial network head and a grading network head. Compared with a single-task model, our multi-task model can provide complementary contextual information, which contributes to better performance. Our model is achieved a state-of-the-art performance in epithelial cells detection and Gleason grading tasks simultaneously. Using fivefold cross-validation, our model is achieved an epithelial cells detection accuracy of 99.07% with an average area under the curve of 0.998. As for Gleason grading, our model is obtained a mean intersection over union of 79.56% and an overall pixel accuracy of 89.40%.
          Cover page: Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2018)
          Automated Gleason grading is an important preliminary step for quantitative histopathological feature extraction. Different from the traditional task of classifying small pre-selected homogeneous regions, semantic segmentation provides pixel-wise Gleason predictions across an entire slide. Deep learning-based segmentation models can automatically learn visual semantics from data, which alleviates the need for feature engineering. However, performance of deep learning models is limited by the scarcity of large-scale fully annotated datasets, which can be both expensive and time-consuming to create. One way to address this problem is to leverage external weakly labeled datasets to augment models trained on the limited data. In this paper, we developed an expectation maximization-based approach constrained by an approximated prior distribution in order to extract useful representations from a large number of weakly labeled images generated from low-magnification annotations. This method was utilized to improve the performance of a model trained on a limited fully annotated dataset. Our semi-supervised approach trained with 135 fully annotated and 1800 weakly annotated tiles achieved a mean Jaccard Index of 49.5% on an independent test set, which was 14% higher than the initial model trained only on the fully annotated dataset.
            Cover page: An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies
            • Article
            • Peer Reviewed
            UCLA Previously Published Works (2022)
            Deep neural networks, in particular convolutional networks, have rapidly become a popular choice for analyzing histopathology images. However, training these models relies heavily on a large number of samples manually annotated by experts, which is cumbersome and expensive. In addition, it is difficult to obtain a perfect set of labels due to the variability between expert annotations. This paper presents a novel active learning (AL) framework for histopathology image analysis, named PathAL. To reduce the required number of expert annotations, PathAL selects two groups of unlabeled data in each training iteration: one "informative" sample that requires additional expert annotation, and one "confident predictive" sample that is automatically added to the training set using the model's pseudo-labels. To reduce the impact of the noisy-labeled samples in the training set, PathAL systematically identifies noisy samples and excludes them to improve the generalization of the model. Our model advances the existing AL method for medical image analysis in two ways. First, we present a selection strategy to improve classification performance with fewer manual annotations. Unlike traditional methods focusing only on finding the most uncertain samples with low prediction confidence, we discover a large number of high confidence samples from the unlabeled set and automatically add them for training with assigned pseudo-labels. Second, we design a method to distinguish between noisy samples and hard samples using a heuristic approach. We exclude the noisy samples while preserving the hard samples to improve model performance. Extensive experiments demonstrate that our proposed PathAL framework achieves promising results on a prostate cancer Gleason grading task, obtaining similar performance with 40% fewer annotations compared to the fully supervised learning scenario. An ablation study is provided to analyze the effectiveness of each component in PathAL, and a pathologist reader study is conducted to validate our proposed algorithm.
              Cover page: PathAL: An Active Learning Framework for Histopathology Image Analysis
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