12 research outputs found
Classification and Retrieval of Digital Pathology Scans: A New Dataset
In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image
classification and retrieval in digital pathology. We use the whole scan images
of 24 different tissue textures to generate 1,325 test patches of size
10001000 (0.5mm0.5mm). Training data can be generated according
to preferences of algorithm designer and can range from approximately 27,000 to
over 50,000 patches if the preset parameters are adopted. We propose a compound
patch-and-scan accuracy measurement that makes achieving high accuracies quite
challenging. In addition, we set the benchmarking line by applying LBP,
dictionary approach and convolutional neural nets (CNNs) and report their
results. The highest accuracy was 41.80\% for CNN.Comment: Accepted for presentation at Workshop for Computer Vision for
Microscopy Image Analysis (CVMI 2017) @ CVPR 2017, Honolulu, Hawai
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
Novel image markers for non-small cell lung cancer classification and survival prediction
BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients.
RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated.
CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers
Novel Image Markers for Non-Small Cell Lung Cancer Classification and Survival Prediction
BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients.
RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated.
CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers
Case-based similar image retrieval for weakly annotated large histopathological images of malignant lymphoma using deep metric learning
In the present study, we propose a novel case-based similar image retrieval
(SIR) method for hematoxylin and eosin (H&E)-stained histopathological images
of malignant lymphoma. When a whole slide image (WSI) is used as an input
query, it is desirable to be able to retrieve similar cases by focusing on
image patches in pathologically important regions such as tumor cells. To
address this problem, we employ attention-based multiple instance learning,
which enables us to focus on tumor-specific regions when the similarity between
cases is computed. Moreover, we employ contrastive distance metric learning to
incorporate immunohistochemical (IHC) staining patterns as useful supervised
information for defining appropriate similarity between heterogeneous malignant
lymphoma cases. In the experiment with 249 malignant lymphoma patients, we
confirmed that the proposed method exhibited higher evaluation measures than
the baseline case-based SIR methods. Furthermore, the subjective evaluation by
pathologists revealed that our similarity measure using IHC staining patterns
is appropriate for representing the similarity of H&E-stained tissue images for
malignant lymphoma
Deep weakly-supervised learning methods for classification and localization in histology images: a survey
Using state-of-the-art deep learning models for cancer diagnosis presents
several challenges related to the nature and availability of labeled histology
images. In particular, cancer grading and localization in these images normally
relies on both image- and pixel-level labels, the latter requiring a costly
annotation process. In this survey, deep weakly-supervised learning (WSL)
models are investigated to identify and locate diseases in histology images,
without the need for pixel-level annotations. Given training data with global
image-level labels, these models allow to simultaneously classify histology
images and yield pixel-wise localization scores, thereby identifying the
corresponding regions of interest (ROI). Since relevant WSL models have mainly
been investigated within the computer vision community, and validated on
natural scene images, we assess the extent to which they apply to histology
images which have challenging properties, e.g. very large size, similarity
between foreground/background, highly unstructured regions, stain
heterogeneity, and noisy/ambiguous labels. The most relevant models for deep
WSL are compared experimentally in terms of accuracy (classification and
pixel-wise localization) on several public benchmark histology datasets for
breast and colon cancer -- BACH ICIAR 2018, BreaKHis, CAMELYON16, and GlaS.
Furthermore, for large-scale evaluation of WSL models on histology images, we
propose a protocol to construct WSL datasets from Whole Slide Imaging. Results
indicate that several deep learning models can provide a high level of
classification accuracy, although accurate pixel-wise localization of cancer
regions remains an issue for such images. Code is publicly available.Comment: 35 pages, 18 figure