Papers Morphology classification
“Morphology classification” 태그가 달린 논문 30편 · 필터 해제
Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
Observational astronomy relies on visual feature identification to detect critical astrophysical phenomena. While machine learning (ML) increasingly automates this process, models often struggle with generalization in la…
AstronomyMorphology classificationobject-detectionObject DetectionAre vision language models robust to uncertain inputs?
Robustness against uncertain and ambiguous inputs is a critical challenge for deep learning models. While recent advancements in large scale vision language models (VLMs, e.g. GPT4o) might suggest that increasing model a…
Anomaly DetectionMorphology classificationUncertainty QuantificationOncolytic mechanisms and immunotherapeutic potential of Newcastle disease virus in cancer therapy
Newcastle Disease Virus (NDV), classified as Avian orthoavulavirus 1 (avian paramyxovirus type 1), is a promising oncolytic agent that selectively targets and destroys cancer cells while sparing normal tissues. Its oncos…
Morphology classificationGalaxy Walker: Geometry-aware VLMs For Galaxy-scale Understanding
Modern vision-language models (VLMs) develop patch embedding and convolution backbone within vector space, especially Euclidean ones, at the very founding. When expanding VLMs to a galaxy scale for understanding astronom…
Mixture-of-ExpertsMorphology classificationFrom Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption
The DESI Legacy Imaging Surveys (DESI-LIS) comprise three distinct surveys: the Dark Energy Camera Legacy Survey (DECaLS), the Beijing-Arizona Sky Survey (BASS), and the Mayall z-band Legacy Survey (MzLS).The citizen sci…
Domain AdaptationMorphology classificationSurveyUnsupervised Domain AdaptationA Versatile Framework for Analyzing Galaxy Image Data by Implanting Human-in-the-loop on a Large Vision Model
The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe. However, effectively analyzing this vast amount of data poses a significant challenge. A…
AstronomyFew-Shot LearningImage RestorationMorphology classification+2SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification
Male infertility accounts for about one-third of global infertility cases. Manual assessment of sperm abnormalities through head morphology analysis encounters issues of observer variability and diagnostic discrepancies …
DiagnosticMorphology classificationE(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification
We propose the use of group convolutional neural network architectures (GCNNs) equivariant to the 2D Euclidean group, $E(2)$, for the task of galaxy morphology classification by utilizing symmetries of the data present i…
Inductive BiasMorphology classificationSpiral-Elliptical automated galaxy morphology classification from telescope images
The classification of galaxy morphologies is an important step in the investigation of theories of hierarchical structure formation. While human expert visual classification remains quite effective and accurate, it canno…
ClassificationMorphology classificationAstroCLIP: A Cross-Modal Foundation Model for Galaxies
We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used - without any model fine-tuning - for a v…
Contrastive LearningmodelMorphology classificationPhotometric Redshift Estimation+3From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation
We present a novel approach for the dimensionality reduction of galaxy images by leveraging a combination of variational auto-encoders (VAE) and domain adaptation (DA). We demonstrate the effectiveness of this approach u…
Dimensionality ReductionDomain AdaptationMorphology classificationDeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-r…
Anomaly DetectionDomain AdaptationMorphology classificationSemi-supervised Domain Adaptation+1Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a dee…
Anomaly DetectionDomain AdaptationMorphology classificationscientific discovery+2The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques
In recent years, large scale data intensive astronomical surveys have resulted in more detailed images being produced than scientists can manually classify. Even attempts to crowd-source this work will soon be outpaced b…
ClusteringMorphology classificationSemi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images
In the field of materials science, microscopy is the first and often only accessible method for structural characterization. There is a growing interest in the development of machine learning methods that can automate th…
BIG-bench Machine LearningInstance SegmentationMorphology classificationSelf-Supervised Learning+1Improving Human Sperm Head Morphology Classification with Unsupervised Anatomical Feature Distillation
With rising male infertility, sperm head morphology classification becomes critical for accurate and timely clinical diagnosis. Recent deep learning (DL) morphology analysis methods achieve promising benchmark results, b…
Morphology classificationSperm Morphology ClassificationDeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification
With increased adoption of supervised deep learning methods for processing and analysis of cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analys…
ClassificationDomain AdaptationImage CompressionMorphology classificationCan semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification?
In this work, we examine the robustness of state-of-the-art semi-supervised learning (SSL) algorithms when applied to morphological classification in modern radio astronomy. We test whether SSL can achieve performance co…
AstronomyMorphology classificationA Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification
The classification of galaxy morphology plays a crucial role in understanding galaxy formation and evolution. Traditionally, this process is done manually. The emergence of deep learning techniques has given room for the…
Deep LearningMorphology classificationMorphological classification of astronomical images with limited labelling
The task of morphological classification is complex for simple parameterization, but important for research in the galaxy evolution field. Future galaxy surveys (e.g. EUCLID) will collect data about more than a $10^9$ ga…
Active LearningAstronomyBinary ClassificationClassification+2