15 research outputs found
Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation
Colorectal cancer is one of the leading cancer death causes worldwide, but its early diagnosis highly improves the survival rates. The success of deep learning has also benefited this clinical field. When training a deep learning model, it is optimized based on the selected loss function. In this work, we consider two networks (U-Net and LinkNet) and two backbones (VGG-16 and Densnet121). We analyzed the influence of seven loss functions and used a principal component analysis (PCA) to determine whether the PCA-based decomposition allows for the defining of the coefficients of a non-redundant primal loss function that can outperform the individual loss functions and different linear combinations. The eigenloss is defined as a linear combination of the individual losses using the elements of the eigenvector as coefficients. Empirical results show that the proposed eigenloss improves the general performance of individual loss functions and outperforms other linear combinations when Linknet is used, showing potential for its application in polyp segmentation problems
The Lov\'asz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
The Jaccard index, also referred to as the intersection-over-union score, is
commonly employed in the evaluation of image segmentation results given its
perceptual qualities, scale invariance - which lends appropriate relevance to
small objects, and appropriate counting of false negatives, in comparison to
per-pixel losses. We present a method for direct optimization of the mean
intersection-over-union loss in neural networks, in the context of semantic
image segmentation, based on the convex Lov\'asz extension of submodular
losses. The loss is shown to perform better with respect to the Jaccard index
measure than the traditionally used cross-entropy loss. We show quantitative
and qualitative differences between optimizing the Jaccard index per image
versus optimizing the Jaccard index taken over an entire dataset. We evaluate
the impact of our method in a semantic segmentation pipeline and show
substantially improved intersection-over-union segmentation scores on the
Pascal VOC and Cityscapes datasets using state-of-the-art deep learning
segmentation architectures.Comment: Accepted as a conference paper at CVPR 201
EmbedTrack—Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths
A systematic analysis of the cell behavior requires automated approaches for
cell segmentation and tracking. While deep learning has been successfully
applied for the task of cell segmentation, there are few approaches for
simultaneous cell segmentation and tracking using deep learning. Here, we
present EmbedTrack, a single convolutional neural network for simultaneous cell
segmentation and tracking which predicts easy to interpret embeddings. As
embeddings, offsets of cell pixels to their cell center and bandwidths are
learned. We benchmark our approach on nine 2D data sets from the Cell Tracking
Challenge, where our approach performs on seven out of nine data sets within
the top 3 contestants including three top 1 performances. The source code is
publicly available at https://git.scc.kit.edu/kit-loe-ge/embedtrack.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Detection and Classification of Local Ca²⁺ Release Events in Cardiomyocytes Using 3D-UNet Neural Network
Global Ca²⁺ increase in the cytosol of cardiomyocytes is crucial for the contraction of the heart. Malfunctioning of proteins involved in this process can trigger local events (e.g., sparks and puffs) and global events (e.g., waves). These are thought to be involved in the development of arrhythmia. Therefore, it is important to detect and classify local Ca²⁺ release events. We present a novel approach, based on a 3D U‐Net architecture, to perform these tasks in a fully automated fashion. We employed data obtained with fast xyt confocal imaging of cardiomyocytes where such subcellular Ca²⁺ events are manually annotated and trained the neural network to infer comparable segmentation as output. Despite the relatively small amount of available data and the challenges that it exhibits, we obtained qualitatively promising results
Loss Functions and Metrics in Deep Learning
One of the essential components of deep learning is the choice of the loss
function and performance metrics used to train and evaluate models. This paper
reviews the most prevalent loss functions and performance measurements in deep
learning. We examine the benefits and limits of each technique and illustrate
their application to various deep-learning problems. Our review aims to give a
comprehensive picture of the different loss functions and performance
indicators used in the most common deep learning tasks and help practitioners
choose the best method for their specific task.Comment: 53 pages, 5 figures, 7 tables, 86 equation
Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels
The soft Dice loss (SDL) has taken a pivotal role in many automated
segmentation pipelines in the medical imaging community. Over the last years,
some reasons behind its superior functioning have been uncovered and further
optimizations have been explored. However, there is currently no implementation
that supports its direct use in settings with soft labels. Hence, a synergy
between the use of SDL and research leveraging the use of soft labels, also in
the context of model calibration, is still missing. In this work, we introduce
Dice semimetric losses (DMLs), which (i) are by design identical to SDL in a
standard setting with hard labels, but (ii) can be used in settings with soft
labels. Our experiments on the public QUBIQ, LiTS and KiTS benchmarks confirm
the potential synergy of DMLs with soft labels (e.g. averaging, label
smoothing, and knowledge distillation) over hard labels (e.g. majority voting
and random selection). As a result, we obtain superior Dice scores and model
calibration, which supports the wider adoption of DMLs in practice. Code is
available at
\href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.Comment: Submitted to MICCAI2023. Code is available at
https://github.com/zifuwanggg/JDTLosse
Multiple Object Tracking in Light Microscopy Images Using Graph-based and Deep Learning Methods
Multi-Objekt-Tracking (MOT) ist ein Problem der Bildanalyse, welches die Lokalisierung und Verknüpfung von Objekten in einer Bildsequenz über die Zeit umfasst, mit zahlreichen Anwendungen in Bereichen wie autonomes Fahren, Robotik oder Überwachung. Neben technischen Anwendungsgebieten besteht auch ein großer Bedarf an MOT in biomedizinischen Anwendungen. So können beispielsweise Experimente, die mittels Lichtmikroskopie über mehrere Stunden oder Tage hinweg erfasst wurden, Hunderte oder sogar Tausende von ähnlich aussehenden Objekten enthalten, was eine manuelle Analyse unmöglich macht. Um jedoch zuverlässige Schlussfolgerungen aus den verfolgten Objekten abzuleiten, ist eine hohe Qualität der prädizierten Trajektorien erforderlich. Daher werden domänenspezifische MOT-Ansätze benötigt, die in der Lage sind, die Besonderheiten von lichtmikroskopischen Daten zu berücksichtigen. In dieser Arbeit werden daher zwei neuartige Methoden für das MOT-Problem in Lichtmikroskopie-Bildern erarbeitet sowie Ansätze zum Vergleich der Tracking-Methoden vorgestellt.
Um die Performanz der Tracking-Methode von der Qualität der Segmentierung zu unterscheiden, wird ein Ansatz vorgeschlagen, der es ermöglicht die Tracking-Methode getrennt von der Segmentierung zu analysieren, was auch eine Untersuchung der Robustheit von Tracking-Methoden gegeben verschlechterter Segmentierungsdaten erlaubt. Des Weiteren wird eine graphbasierte Tracking-Methode vorgeschlagen, welche eine Brücke zwischen einfach anzuwendenden, aber weniger performanten Tracking-Methoden und performanten Tracking-Methoden mit vielen schwer einstellbaren Parametern schlägt. Die vorgeschlagene Tracking-Methode hat nur wenige manuell einstellbare Parameter und ist einfach auf 2D- und 3D-Datensätze anwendbar. Durch die Modellierung von Vorwissen über die Form des Tracking-Graphen ist die vorgeschlagene Tracking-Methode außerdem in der Lage, bestimmte Arten von Segmentierungsfehlern automatisch zu korrigieren. Darüber hinaus wird ein auf Deep Learning basierender Ansatz vorgeschlagen, der die Aufgabe der Instanzsegmentierung und Objektverfolgung gleichzeitig in einem einzigen neuronalen Netzwerk erlernt. Außerdem lernt der vorgeschlagene Ansatz Repräsentationen zu prädizieren, die für den Menschen verständlich sind. Um die Performanz der beiden vorgeschlagenen Tracking-Methoden im Vergleich zu anderen aktuellen, domänenspezifischen Tracking-Ansätzen zu zeigen, werden sie auf einen domänenspezifischen Benchmark angewendet. Darüber hinaus werden weitere Bewertungskriterien für Tracking-Methoden eingeführt, welche zum Vergleich der beiden vorgeschlagenen Tracking-Methoden herangezogen werden
Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union
Semantic segmentation datasets often exhibit two types of imbalance:
\textit{class imbalance}, where some classes appear more frequently than others
and \textit{size imbalance}, where some objects occupy more pixels than others.
This causes traditional evaluation metrics to be biased towards
\textit{majority classes} (e.g. overall pixel-wise accuracy) and \textit{large
objects} (e.g. mean pixel-wise accuracy and per-dataset mean intersection over
union). To address these shortcomings, we propose the use of fine-grained mIoUs
along with corresponding worst-case metrics, thereby offering a more holistic
evaluation of segmentation techniques. These fine-grained metrics offer less
bias towards large objects, richer statistical information, and valuable
insights into model and dataset auditing. Furthermore, we undertake an
extensive benchmark study, where we train and evaluate 15 modern neural
networks with the proposed metrics on 12 diverse natural and aerial
segmentation datasets. Our benchmark study highlights the necessity of not
basing evaluations on a single metric and confirms that fine-grained mIoUs
reduce the bias towards large objects. Moreover, we identify the crucial role
played by architecture designs and loss functions, which lead to best practices
in optimizing fine-grained metrics. The code is available at
\href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.Comment: NeurIPS 202
DET: Data Enhancement Technique for Aerial Images
Deep learning and computer vision are two thriving research areas within machine learning.
In recent years, as the available computing power has grown, it has led to the possibility
of combining the approaches, achieving state-of-the-art results. An area of research that
has greatly benefited from this development is building detection. Although the algorithms
produce satisfactory results, there are still many limitations. One significant problem is the
quality and edge sharpness of the segmentation masks, which are not up to the standard
required by the mapping industry. The predicted mask boundaries need to be sharper and
more precise to have practical use in map production.
This thesis introduces a novel Data Enhancement Technique (DET) to improve the boundary
quality of segmentation masks. DET has two approaches, Seg-DET, which uses a segmentation
network, and Edge-DET, which uses an edge-detection network. Both techniques
highlight buildings, creating a better input foundation for a secondary segmentation model.
Additionally, we introduce ABL(RMI), a new compounding loss consisting of Region Mutual
Information Loss (RMI), Lovasz-Softmax Loss (Lovasz), and Active Boundary Loss (ABL).
The combination of loss functions in ABL(RMI) is optimized to enhance and improve mask
boundaries.
This thesis empirically shows that DET can successfully improve segmentation boundaries,
but the practical results suggest that further refinement is needed. Additionally, the results
show improvements when using the new compounding loss ABL(RMI) compared to its
predecessor, ABL(CE) which substitutes RMI with Cross-Entropy loss(CE)