97 research outputs found
Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks
Microscopic analysis of histological sections is considered the "gold
standard" to verify structural parcellations in the human brain. Its high
resolution allows the study of laminar and columnar patterns of cell
distributions, which build an important basis for the simulation of cortical
areas and networks. However, such cytoarchitectonic mapping is a semiautomatic,
time consuming process that does not scale with high throughput imaging. We
present an automatic approach for parcellating histological sections at 2um
resolution. It is based on a convolutional neural network that combines
topological information from probabilistic atlases with the texture features
learned from high-resolution cell-body stained images. The model is applied to
visual areas and trained on a sparse set of partial annotations. We show how
predictions are transferable to new brains and spatially consistent across
sections.Comment: Accepted for oral presentation at International Symposium of
Biomedical Imaging (ISBI) 201
Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds
Image denoising can be described as the problem of mapping from a noisy image
to a noise-free image. The best currently available denoising methods
approximate this mapping with cleverly engineered algorithms. In this work we
attempt to learn this mapping directly with plain multi layer perceptrons (MLP)
applied to image patches. We will show that by training on large image
databases we are able to outperform the current state-of-the-art image
denoising methods. In addition, our method achieves results that are superior
to one type of theoretical bound and goes a large way toward closing the gap
with a second type of theoretical bound. Our approach is easily adapted to less
extensively studied types of noise, such as mixed Poisson-Gaussian noise, JPEG
artifacts, salt-and-pepper noise and noise resembling stripes, for which we
achieve excellent results as well. We will show that combining a block-matching
procedure with MLPs can further improve the results on certain images. In a
second paper, we detail the training trade-offs and the inner mechanisms of our
MLPs
How to Explain Individual Classification Decisions
After building a classifier with modern tools of machine learning we
typically have a black box at hand that is able to predict well for unseen
data. Thus, we get an answer to the question what is the most likely label of a
given unseen data point. However, most methods will provide no answer why the
model predicted the particular label for a single instance and what features
were most influential for that particular instance. The only method that is
currently able to provide such explanations are decision trees. This paper
proposes a procedure which (based on a set of assumptions) allows to explain
the decisions of any classification method.Comment: 31 pages, 14 figure
Contour Proposal Networks for Biomedical Instance Segmentation
We present a conceptually simple framework for object instance segmentation
called Contour Proposal Network (CPN), which detects possibly overlapping
objects in an image while simultaneously fitting closed object contours using
an interpretable, fixed-sized representation based on Fourier Descriptors. The
CPN can incorporate state of the art object detection architectures as backbone
networks into a single-stage instance segmentation model that can be trained
end-to-end. We construct CPN models with different backbone networks, and apply
them to instance segmentation of cells in datasets from different modalities.
In our experiments, we show CPNs that outperform U-Nets and Mask R-CNNs in
instance segmentation accuracy, and present variants with execution times
suitable for real-time applications. The trained models generalize well across
different domains of cell types. Since the main assumption of the framework are
closed object contours, it is applicable to a wide range of detection problems
also outside the biomedical domain. An implementation of the model architecture
in PyTorch is freely available
- …