15,250 research outputs found
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
Learning to Discover Sparse Graphical Models
We consider structure discovery of undirected graphical models from
observational data. Inferring likely structures from few examples is a complex
task often requiring the formulation of priors and sophisticated inference
procedures. Popular methods rely on estimating a penalized maximum likelihood
of the precision matrix. However, in these approaches structure recovery is an
indirect consequence of the data-fit term, the penalty can be difficult to
adapt for domain-specific knowledge, and the inference is computationally
demanding. By contrast, it may be easier to generate training samples of data
that arise from graphs with the desired structure properties. We propose here
to leverage this latter source of information as training data to learn a
function, parametrized by a neural network that maps empirical covariance
matrices to estimated graph structures. Learning this function brings two
benefits: it implicitly models the desired structure or sparsity properties to
form suitable priors, and it can be tailored to the specific problem of edge
structure discovery, rather than maximizing data likelihood. Applying this
framework, we find our learnable graph-discovery method trained on synthetic
data generalizes well: identifying relevant edges in both synthetic and real
data, completely unknown at training time. We find that on genetics, brain
imaging, and simulation data we obtain performance generally superior to
analytical methods
Assessment of algorithms for mitosis detection in breast cancer histopathology images
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues.
In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists
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