331 research outputs found
Denoising Diffusion Medical Models
In this study, we introduce a generative model that can synthesize a large
number of radiographical image/label pairs, and thus is asymptotically
favorable to downstream activities such as segmentation in bio-medical image
analysis. Denoising Diffusion Medical Model (DDMM), the proposed technique, can
create realistic X-ray images and associated segmentations on a small number of
annotated datasets as well as other massive unlabeled datasets with no
supervision. Radiograph/segmentation pairs are generated jointly by the DDMM
sampling process in probabilistic mode. As a result, a vanilla UNet that uses
this data augmentation for segmentation task outperforms other similarly
data-centric approaches.Comment: Accepted to IEEE ISBI 202
Méthodologie de synthèse d'imidazoles et de benzimidazoles. Approche de synthèse de la benzosceptrine et évaluation biologique.
Methodology of addition-cyclization of guanidines and amidines on quinones for the synthesis of 2-amino-benzimidazole. Applying this strategy to the synthesis of benzo-bis-2-aminoimmidazole, an analogue of benzosceptrine.Reclamation of products synthesized by biological evaluation: inhibition of kinase, cytotoxicity on cancer cell lines of blood.Méthodologie d'addition-cyclisation de guanidines et amidines sur les quinones pour la synthèse de 2-aminobenzimidazole. Application de cette stratégie à la synthèse de benzo-bis-2-aminoimmidazole, un motif important de la benzosceptrine.Valorisation de produits synthétisés par évaluation biologique : l'inhibition de kinase, la cytotoxicité sur les lignées cellulaires cancéreuses du sang
Towards Fast and High-quality Biomedical Image Reconstruction
Department of Computer Science and EngineeringReconstruction is an important module in the image analysis pipeline with purposes of isolating the majority of meaningful information that hidden inside the acquired data. The term ???reconstruction??? can be understood and subdivided in several specific tasks in different modalities. For example, in biomedical imaging, such as Computed Tomography (CT), Magnetic Resonance Image (MRI), that term stands for the transformation from the, possibly fully or under-sampled, spectral domains (sinogram for CT and k-space for MRI) to the visible image domains. Or, in connectomics, people usually refer it to segmentation (reconstructing the semantic contact between neuronal connections) or denoising (reconstructing the clean image). In this dissertation research, I will describe a set of my contributed algorithms from conventional to state-of-the-art deep learning methods, with a transition at the data-driven dictionary learning approaches that tackle the reconstruction problems in various image analysis tasks.clos
Voltage Stability Monitoring based on Adaptive Dynamic Mode Decomposition
This paper develops a new voltage stability monitoring method using dynamic mode decomposition (DMD) and its adaptive variance. First, state estimation (SE) is used to estimate the voltage in the system. Then, the measured voltages from the phasor measurement units (PMU) and estimations from SE are used as the inputs for DMD to predict the long-term voltage dynamic. Furthermore, to improve the prediction performance, the normal DMD is improved by adaptively changing the size of input samples based on the error in the training phase, named adaptive DMD (ADMD). The effectiveness of the proposed method is validated on the Nordic32 test system, which is recommended as the test system for voltage stability studies. Different contingency scenarios are used, and the results show that the proposed method is able to monitor the voltage stability after a disturbance (i.e., 4.3x10-4 MAPE for a stable case and 0.0041 MAPE for an unstable case). Furthermore, the results from a scenario in which a real-world oscillation event is used show high accuracy in voltage stability monitoring of the proposed ADMD method
Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks
Realizing complete observability in the three-phase distribution system
remains a challenge that hinders the implementation of classic state estimation
algorithms. In this paper, a new method, called the pruned physics-aware neural
network (P2N2), is developed to improve the voltage estimation accuracy in the
distribution system. The method relies on the physical grid topology, which is
used to design the connections between different hidden layers of a neural
network model. To verify the proposed method, a numerical simulation based on
one-year smart meter data of load consumptions for three-phase power flow is
developed to generate the measurement and voltage state data. The IEEE 123-node
system is selected as the test network to benchmark the proposed algorithm
against the classic weighted least squares (WLS). Numerical results show that
P2N2 outperforms WLS in terms of data redundancy and estimation accuracy
GUNNEL: Guided Mixup Augmentation and Multi-View Fusion for Aquatic Animal Segmentation
Recent years have witnessed great advances in object segmentation research.
In addition to generic objects, aquatic animals have attracted research
attention. Deep learning-based methods are widely used for aquatic animal
segmentation and have achieved promising performance. However, there is a lack
of challenging datasets for benchmarking. In this work, we build a new dataset
dubbed "Aquatic Animal Species." We also devise a novel GUided mixup
augmeNtatioN and multi-viEw fusion for aquatic animaL segmentation (GUNNEL)
that leverages the advantages of multiple view segmentation models to
effectively segment aquatic animals and improves the training performance by
synthesizing hard samples. Extensive experiments demonstrated the superiority
of our proposed framework over existing state-of-the-art instance segmentation
methods
MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation
Few-shot instance segmentation extends the few-shot learning paradigm to the
instance segmentation task, which tries to segment instance objects from a
query image with a few annotated examples of novel categories. Conventional
approaches have attempted to address the task via prototype learning, known as
point estimation. However, this mechanism depends on prototypes (\eg mean of
shot) for prediction, leading to performance instability. To overcome the
disadvantage of the point estimation mechanism, we propose a novel approach,
dubbed MaskDiff, which models the underlying conditional distribution of a
binary mask, which is conditioned on an object region and shot information.
Inspired by augmentation approaches that perturb data with Gaussian noise for
populating low data density regions, we model the mask distribution with a
diffusion probabilistic model. We also propose to utilize classifier-free
guided mask sampling to integrate category information into the binary mask
generation process. Without bells and whistles, our proposed method
consistently outperforms state-of-the-art methods on both base and novel
classes of the COCO dataset while simultaneously being more stable than
existing methods. The source code is available at:
https://github.com/minhquanlecs/MaskDiff.Comment: Accepted at AAAI 2024 (oral presentation
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