4 research outputs found
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation
Deep convolutional neural networks (CNNs) have shown excellent performance in
object recognition tasks and dense classification problems such as semantic
segmentation. However, training deep neural networks on large and sparse
datasets is still challenging and can require large amounts of computation and
memory. In this work, we address the task of performing semantic segmentation
on large data sets, such as three-dimensional medical images. We propose an
adaptive sampling scheme that uses a-posterior error maps, generated throughout
training, to focus sampling on difficult regions, resulting in improved
learning. Our contribution is threefold: 1) We give a detailed description of
the proposed sampling algorithm to speed up and improve learning performance on
large images. We propose a deep dual path CNN that captures information at fine
and coarse scales, resulting in a network with a large field of view and high
resolution outputs. We show that our method is able to attain new
state-of-the-art results on the VISCERAL Anatomy benchmark
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Background and objective:
Processing of medical images such as MRI or CT presents different challenges compared to RGB images typically used in computer vision. These include a lack of labels for large datasets, high computational costs, and the need of metadata to describe the physical properties of voxels. Data augmentation is used to artificially increase the size of the training datasets. Training with image subvolumes or patches decreases the need for computational power. Spatial metadata needs to be carefully taken into account in order to ensure a correct alignment and orientation of volumes.
Methods:
We present TorchIO, an open-source Python library to enable efficient loading, preprocessing, augmentation and patch-based sampling of medical images for deep learning. TorchIO follows the style of PyTorch and integrates standard medical image processing libraries to efficiently process images during training of neural networks. TorchIO transforms can be easily composed, reproduced, traced and extended. Most transforms can be inverted, making the library suitable for test-time augmentation and estimation of aleatoric uncertainty in the context of segmentation. We provide multiple generic preprocessing and augmentation operations as well as simulation of MRI-specific artifacts.
Results:
Source code, comprehensive tutorials and extensive documentation for TorchIO can be found at http://torchio.rtfd.io/. The package can be installed from the Python Package Index (PyPI) running pip install torchio. It includes a command-line interface which allows users to apply transforms to image files without using Python. Additionally, we provide a graphical user interface within a TorchIO extension in 3D Slicer to visualize the effects of transforms.
Conclusion:
TorchIO was developed to help researchers standardize medical image processing pipelines and allow them to focus on the deep learning experiments. It encourages good open-science practices, as it supports experiment reproducibility and is version-controlled so that the software can be cited precisely. Due to its modularity, the library is compatible with other frameworks for deep learning with medical images
Distributionally Robust Deep Learning using Hardness Weighted Sampling
Limiting failures of machine learning systems is vital for safety-critical
applications. In order to improve the robustness of machine learning systems,
Distributionally Robust Optimization (DRO) has been proposed as a
generalization of Empirical Risk Minimization (ERM)aiming at addressing this
need. However, its use in deep learning has been severely restricted due to the
relative inefficiency of the optimizers available for DRO in comparison to the
wide-spread variants of Stochastic Gradient Descent (SGD) optimizers for ERM.
We propose SGD with hardness weighted sampling, a principled and efficient
optimization method for DRO in machine learning that is particularly suited in
the context of deep learning. Similar to a hard example mining strategy in
essence and in practice, the proposed algorithm is straightforward to implement
and computationally as efficient as SGD-based optimizers used for deep
learning, requiring minimal overhead computation. In contrast to typical ad hoc
hard mining approaches, and exploiting recent theoretical results in deep
learning optimization, we prove the convergence of our DRO algorithm for
over-parameterized deep learning networks with ReLU activation and finite
number of layers and parameters. Our experiments on brain tumor segmentation in
MRI demonstrate the feasibility and the usefulness of our approach. Using our
hardness weighted sampling leads to a decrease of 2% of the interquartile range
of the Dice scores for the enhanced tumor and the tumor core regions. The code
for the proposed hard weighted sampler will be made publicly available