519 research outputs found
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
Joint and individual analysis of breast cancer histologic images and genomic covariates
A key challenge in modern data analysis is understanding connections between
complex and differing modalities of data. For example, two of the main
approaches to the study of breast cancer are histopathology (analyzing visual
characteristics of tumors) and genetics. While histopathology is the gold
standard for diagnostics and there have been many recent breakthroughs in
genetics, there is little overlap between these two fields. We aim to bridge
this gap by developing methods based on Angle-based Joint and Individual
Variation Explained (AJIVE) to directly explore similarities and differences
between these two modalities. Our approach exploits Convolutional Neural
Networks (CNNs) as a powerful, automatic method for image feature extraction to
address some of the challenges presented by statistical analysis of
histopathology image data. CNNs raise issues of interpretability that we
address by developing novel methods to explore visual modes of variation
captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features.
Our results provide many interpretable connections and contrasts between
histopathology and genetics
Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
Automated classification of histopathological whole-slide images (WSI) of
breast tissue requires analysis at very high resolutions with a large
contextual area. In this paper, we present context-aware stacked convolutional
neural networks (CNN) for classification of breast WSIs into normal/benign,
ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first
train a CNN using high pixel resolution patches to capture cellular level
information. The feature responses generated by this model are then fed as
input to a second CNN, stacked on top of the first. Training of this stacked
architecture with large input patches enables learning of fine-grained
(cellular) details and global interdependence of tissue structures. Our system
is trained and evaluated on a dataset containing 221 WSIs of H&E stained breast
tissue specimens. The system achieves an AUC of 0.962 for the binary
classification of non-malignant and malignant slides and obtains a three class
accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC,
demonstrating its potentials for routine diagnostics
Machine-learning-based evaluation of intratumoral heterogeneity and tumor-stroma interface for clinical guidance
© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Laurinavicius, A., Rasmusson, A., Plancoulaine, B., Shribak, M., & Levenson, R. Machine-learning-based evaluation of intratumoral heterogeneity and tumor-stroma interface for clinical guidance. American Journal of Pathology, 191(10), (2021): 1724–1731, https://doi.org/10.1016/j.ajpath.2021.04.008.Assessment of intratumoral heterogeneity and tumor-host interaction within the tumor microenvironment is becoming increasingly important for innovative cancer therapy decisions because of the unique information it can generate about the state of the disease. However, its assessment and quantification are limited by ambiguous definitions of the tumor-host interface and by human cognitive capacity in current pathology practice. Advances in machine learning and artificial intelligence have opened the field of digital pathology to novel tissue image analytics and feature extraction for generation of high-capacity computational disease management models. A particular benefit is expected from machine-learning applications that can perform extraction and quantification of subvisual features of both intratumoral heterogeneity and tumor microenvironment aspects. These methods generate information about cancer cell subpopulation heterogeneity, potential tumor-host interactions, and tissue microarchitecture, derived from morphologically resolved content using both explicit and implicit features. Several studies have achieved promising diagnostic, prognostic, and predictive artificial intelligence models that often outperform current clinical and pathology criteria. However, further effort is needed for clinical adoption of such methods through development of standardizable high-capacity workflows and proper validation studies.Supported by the European Social Fund grant 09.3.3-LMT-K-712
Learning to Segment Breast Biopsy Whole Slide Images
We trained and applied an encoder-decoder model to semantically segment
breast biopsy images into biologically meaningful tissue labels. Since
conventional encoder-decoder networks cannot be applied directly on large
biopsy images and the different sized structures in biopsies present novel
challenges, we propose four modifications: (1) an input-aware encoding block to
compensate for information loss, (2) a new dense connection pattern between
encoder and decoder, (3) dense and sparse decoders to combine multi-level
features, (4) a multi-resolution network that fuses the results of
encoder-decoders run on different resolutions. Our model outperforms a
feature-based approach and conventional encoder-decoders from the literature.
We use semantic segmentations produced with our model in an automated diagnosis
task and obtain higher accuracies than a baseline approach that employs an SVM
for feature-based segmentation, both using the same segmentation-based
diagnostic features.Comment: Added more WSI images in appendi
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