43 research outputs found
Fully Unsupervised Image Denoising, Diversity Denoising and Image Segmentation with Limited Annotations
Understanding the processes of cellular development and the interplay of cell shape changes, division and migration requires investigation of developmental processes at the spatial resolution of single cell. Biomedical imaging experiments enable the study of dynamic processes as they occur in living organisms. While biomedical imaging is essential, a key component of exposing unknown biological phenomena is quantitative image analysis. Biomedical images, especially microscopy images, are usually noisy owing to practical limitations such as available photon budget, sample sensitivity, etc. Additionally, microscopy images often contain artefacts due to the optical aberrations in microscopes or due to imperfections in camera sensor and internal electronics. The noisy nature of images as well as the artefacts prohibit accurate downstream analysis such as cell segmentation. Although countless approaches have been proposed for image denoising, artefact removal and segmentation, supervised Deep Learning (DL) based content-aware algorithms are currently the best performing for all these tasks.
Supervised DL based methods are plagued by many practical limitations. Supervised denoising and artefact removal algorithms require paired corrupted and high quality images for training. Obtaining such image pairs can be very hard and virtually impossible in most biomedical imaging applications owing to photosensitivity and the dynamic nature of the samples being imaged. Similarly, supervised DL based segmentation methods need copious amounts of annotated data for training, which is often very expensive to obtain. Owing to these restrictions, it is imperative to look beyond supervised methods. The objective of this thesis is to develop novel unsupervised alternatives for image denoising, and artefact removal as well as semisupervised approaches for image segmentation.
The first part of this thesis deals with unsupervised image denoising and artefact removal. For unsupervised image denoising task, this thesis first introduces a probabilistic approach for training DL based methods using parametric models of imaging noise. Next, a novel unsupervised diversity denoising framework is presented which addresses the fundamentally non-unique inverse nature of image denoising by generating multiple plausible denoised solutions for any given noisy image. Finally, interesting properties of the diversity denoising methods are presented which make them suitable for unsupervised spatial artefact removal in microscopy and medical imaging applications.
In the second part of this thesis, the problem of cell/nucleus segmentation is addressed. The focus is especially on practical scenarios where ground truth annotations for training DL based segmentation methods are scarcely available. Unsupervised denoising is used as an aid to improve segmentation performance in the presence of limited annotations. Several training strategies are presented in this work to leverage the representations learned by unsupervised denoising networks to enable better cell/nucleus segmentation in microscopy data. Apart from DL based segmentation methods, a proof-of-concept is introduced which views cell/nucleus segmentation from the perspective of solving a label fusion problem. This method, through limited human interaction, learns to choose the best possible segmentation for each cell/nucleus using only a pool of diverse (and possibly faulty) segmentation hypotheses as input.
In summary, this thesis seeks to introduce new unsupervised denoising and artefact removal methods as well as semi-supervised segmentation methods which can be easily deployed to directly and immediately benefit biomedical practitioners with their research
Distributed Apportioning in a Power Network for providing Demand Response Services
Greater penetration of Distributed Energy Resources (DERs) in power networks
requires coordination strategies that allow for self-adjustment of
contributions in a network of DERs, owing to variability in generation and
demand. In this article, a distributed scheme is proposed that enables a DER in
a network to arrive at viable power reference commands that satisfies the DERs
local constraints on its generation and loads it has to service, while, the
aggregated behavior of multiple DERs in the network and their respective loads
meet the ancillary services demanded by the grid. The Net-load Management
system for a single unit is referred to as the Local Inverter System (LIS) in
this article . A distinguishing feature of the proposed consensus based
solution is the distributed finite time termination of the algorithm that
allows each LIS unit in the network to determine power reference commands in
the presence of communication delays in a distributed manner. The proposed
scheme allows prioritization of Renewable Energy Sources (RES) in the network
and also enables auto-adjustment of contributions from LIS units with lower
priority resources (non-RES). The methods are validated using
hardware-in-the-loop simulations with Raspberry PI devices as distributed
control units, implementing the proposed distributed algorithm and responsible
for determining and dispatching realtime power reference commands to simulated
power electronics interface emulating LIS units for demand response.Comment: 7 pages, 11 Figures, IEEE International Conference on Smart Grid
Communication
Fully Unsupervised Probabilistic Noise2Void
Image denoising is the first step in many biomedical image analysis pipelines
and Deep Learning (DL) based methods are currently best performing. A new
category of DL methods such as Noise2Void or Noise2Self can be used fully
unsupervised, requiring nothing but the noisy data. However, this comes at the
price of reduced reconstruction quality. The recently proposed Probabilistic
Noise2Void (PN2V) improves results, but requires an additional noise model for
which calibration data needs to be acquired. Here, we present improvements to
PN2V that (i) replace histogram based noise models by parametric noise models,
and (ii) show how suitable noise models can be created even in the absence of
calibration data. This is a major step since it actually renders PN2V fully
unsupervised. We demonstrate that all proposed improvements are not only
academic but indeed relevant.Comment: Accepted at ISBI 202
DenoiSeg: Joint Denoising and Segmentation
Microscopy image analysis often requires the segmentation of objects, but
training data for this task is typically scarce and hard to obtain. Here we
propose DenoiSeg, a new method that can be trained end-to-end on only a few
annotated ground truth segmentations. We achieve this by extending Noise2Void,
a self-supervised denoising scheme that can be trained on noisy images alone,
to also predict dense 3-class segmentations. The reason for the success of our
method is that segmentation can profit from denoising, especially when
performed jointly within the same network. The network becomes a denoising
expert by seeing all available raw data, while co-learning to segment, even if
only a few segmentation labels are available. This hypothesis is additionally
fueled by our observation that the best segmentation results on high quality
(very low noise) raw data are obtained when moderate amounts of synthetic noise
are added. This renders the denoising-task non-trivial and unleashes the
desired co-learning effect. We believe that DenoiSeg offers a viable way to
circumvent the tremendous hunger for high quality training data and effectively
enables few-shot learning of dense segmentations.Comment: 10 pages, 4 figures, 2 pages supplement (4 figures
Addressing label noise for electronic health records: insights from computer vision for tabular data
The analysis of extensive electronic health records (EHR) datasets often calls for automated solutions, with machine learning (ML) techniques, including deep learning (DL), taking a lead role. One common task involves categorizing EHR data into predefined groups. However, the vulnerability of EHRs to noise and errors stemming from data collection processes, as well as potential human labeling errors, poses a significant risk. This risk is particularly prominent during the training of DL models, where the possibility of overfitting to noisy labels can have serious repercussions in healthcare. Despite the well-documented existence of label noise in EHR data, few studies have tackled this challenge within the EHR domain. Our work addresses this gap by adapting computer vision (CV) algorithms to mitigate the impact of label noise in DL models trained on EHR data. Notably, it remains uncertain whether CV methods, when applied to the EHR domain, will prove effective, given the substantial divergence between the two domains. We present empirical evidence demonstrating that these methods, whether used individually or in combination, can substantially enhance model performance when applied to EHR data, especially in the presence of noisy/incorrect labels. We validate our methods and underscore their practical utility in real-world EHR data, specifically in the context of COVID-19 diagnosis. Our study highlights the effectiveness of CV methods in the EHR domain, making a valuable contribution to the advancement of healthcare analytics and research
Leveraging Self-supervised Denoising for Image Segmentation
Deep learning (DL) has arguably emerged as the method of choice for the
detection and segmentation of biological structures in microscopy images.
However, DL typically needs copious amounts of annotated training data that is
for biomedical projects typically not available and excessively expensive to
generate. Additionally, tasks become harder in the presence of noise, requiring
even more high-quality training data. Hence, we propose to use denoising
networks to improve the performance of other DL-based image segmentation
methods. More specifically, we present ideas on how state-of-the-art
self-supervised CARE networks can improve cell/nuclei segmentation in
microscopy data. Using two state-of-the-art baseline methods, U-Net and
StarDist, we show that our ideas consistently improve the quality of resulting
segmentations, especially when only limited training data for noisy micrographs
are available.Comment: accepted at ISBI 202
A Primal-Dual Solver for Large-Scale Tracking-by-Assignment
We propose a fast approximate solver for the combinatorial problem known as
tracking-by-assignment, which we apply to cell tracking. The latter plays a key
role in discovery in many life sciences, especially in cell and developmental
biology. So far, in the most general setting this problem was addressed by
off-the-shelf solvers like Gurobi, whose run time and memory requirements
rapidly grow with the size of the input. In contrast, for our method this
growth is nearly linear.
Our contribution consists of a new (1) decomposable compact representation of
the problem; (2) dual block-coordinate ascent method for optimizing the
decomposition-based dual; and (3) primal heuristics that reconstructs a
feasible integer solution based on the dual information. Compared to solving
the problem with Gurobi, we observe an up to~60~times speed-up, while reducing
the memory footprint significantly. We demonstrate the efficacy of our method
on real-world tracking problems.Comment: 23rd International Conference on Artificial Intelligence and
Statistics (AISTATS), 202
Probabilistic Noise2Void: Unsupervised Content-Aware Denoising
Today, Convolutional Neural Networks (CNNs) are the leading method for image denoising. They are traditionally trained on pairs of images, which are often hard to obtain for practical applications. This motivates self-supervised training methods, such as Noise2Void (N2V) that operate on single noisy images. Self-supervised methods are, unfortunately, not competitive with models trained on image pairs. Here, we present Probabilistic Noise2Void (PN2V), a method to train CNNs to predict per-pixel intensity distributions. Combining these with a suitable description of the noise, we obtain a complete probabilistic model for the noisy observations and true signal in every pixel. We evaluate PN2V on publicly available microscopy datasets, under a broad range of noise regimes, and achieve competitive results with respect to supervised state-of-the-art methods