19 research outputs found
A small note on variation in segmentation annotations
We report on the results of a small crowdsourcing experiment conducted at a
workshop on machine learning for segmentation held at the Danish Bio Imaging
network meeting 2020. During the workshop we asked participants to manually
segment mitochondria in three 2D patches. The aim of the experiment was to
illustrate that manual annotations should not be seen as the ground truth, but
as a reference standard that is subject to substantial variation. In this note
we show how the large variation we observed in the segmentations can be reduced
by removing the annotators with worst pair-wise agreement. Having removed the
annotators with worst performance, we illustrate that the remaining variance is
semantically meaningful and can be exploited to obtain segmentations of cell
boundary and cell interior
Locally orderless tensor networks for classifying two- and three-dimensional medical images
Tensor networks are factorisations of high rank tensors into networks of
lower rank tensors and have primarily been used to analyse quantum many-body
problems. Tensor networks have seen a recent surge of interest in relation to
supervised learning tasks with a focus on image classification. In this work,
we improve upon the matrix product state (MPS) tensor networks that can operate
on one-dimensional vectors to be useful for working with 2D and 3D medical
images. We treat small image regions as orderless, squeeze their spatial
information into feature dimensions and then perform MPS operations on these
locally orderless regions. These local representations are then aggregated in a
hierarchical manner to retain global structure. The proposed locally orderless
tensor network (LoTeNet) is compared with relevant methods on three datasets.
The architecture of LoTeNet is fixed in all experiments and we show it requires
lesser computational resources to attain performance on par or superior to the
compared methods.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) (see https://melba-journal.org). Source code at
https://github.com/raghavian/LoTeNet_pytorch
Deep Learning from Label Proportions for Emphysema Quantification
We propose an end-to-end deep learning method that learns to estimate
emphysema extent from proportions of the diseased tissue. These proportions
were visually estimated by experts using a standard grading system, in which
grades correspond to intervals (label example: 1-5% of diseased tissue). The
proposed architecture encodes the knowledge that the labels represent a
volumetric proportion. A custom loss is designed to learn with intervals. Thus,
during training, our network learns to segment the diseased tissue such that
its proportions fit the ground truth intervals. Our architecture and loss
combined improve the performance substantially (8% ICC) compared to a more
conventional regression network. We outperform traditional lung densitometry
and two recently published methods for emphysema quantification by a large
margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance.
Moreover, our method generates emphysema segmentations that predict the spatial
distribution of emphysema at human level.Comment: Accepted to MICCAI 201
Multi-layered tensor networks for image classification
The recently introduced locally orderless tensor network (LoTeNet) for
supervised image classification uses matrix product state (MPS) operations on
grids of transformed image patches. The resulting patch representations are
combined back together into the image space and aggregated hierarchically using
multiple MPS blocks per layer to obtain the final decision rules. In this work,
we propose a non-patch based modification to LoTeNet that performs one MPS
operation per layer, instead of several patch-level operations. The spatial
information in the input images to MPS blocks at each layer is squeezed into
the feature dimension, similar to LoTeNet, to maximise retained spatial
correlation between pixels when images are flattened into 1D vectors. The
proposed multi-layered tensor network (MLTN) is capable of learning linear
decision boundaries in high dimensional spaces in a multi-layered setting,
which results in a reduction in the computation cost compared to LoTeNet
without any degradation in performance.Comment: Updated version with exact computation costs. 6 pages. Accepted to
the First Workshop on Quantum Tensor Networks in Machine Learning. In
conjunction with 34th NeurIPS, 2020. Source code at
https://github.com/raghavian/mlt