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Found 13 papers, 7 papers with code
Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation
In the field of image classification, existing methods often struggle with biased or ambiguous data, a prevalent issue in real-world scenarios.
Label Smarter, Not Harder: CleverLabel for Faster Annotation of Ambiguous Image Classification with Higher Quality
High-quality data is crucial for the success of machine learning, but labeling large datasets is often a time-consuming and costly process.
Opportunistic hip fracture risk prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study
We show that FORM can correctly predict the 10-year hip fracture risk with a validation AUC of 81. 44 +- 3. 11% / 81. 04 +- 5. 54% (mean +- STD) including additional information like age, BMI, fall history and health background across a 5-fold cross validation on the X-ray and CT cohort, respectively.
Beyond Hard Labels: Investigating data label distributions
High-quality data is a key aspect of modern machine learning.
Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation
We propose a data-centric image classification benchmark with ten real-world datasets and multiple annotations per image to allow researchers to investigate and quantify the impact of such data quality issues.
Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy
We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels.
Life is not black and white -- Combining Semi-Supervised Learning with fuzzy labels
We envision the incorporation of fuzzy labels into Semi-Supervised Learning and give a proof-of-concept of the potential lower costs and higher consistency in the complete development cycle.
S2C2 - An orthogonal method for Semi-Supervised Learning on ambiguous labels
Semi-Supervised Learning (SSL) can decrease the required amount of labeled image data and thus the cost for deep learning.
Learning Stixel-based Instance Segmentation
Stixels have been successfully applied to a wide range of vision tasks in autonomous driving, recently including instance segmentation.
A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering
In our data-centric approach, we propose a method to relabel such ambiguous labels instead of implementing the handling of this issue in a neural network.
Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclustering
We propose a novel loss to improve the overclustering capability of our framework and show on the common image classification dataset STL-10 that it is faster and has better overclustering performance than previous work.
A survey on Semi-, Self- and Unsupervised Learning for Image Classification
In this survey, we provide an overview of often used ideas and methods in image classification with fewer labels.
2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy
Furthermore, we compare a variety of 2D and 3D methods such as classical approaches like Fourier analysis with state-of-the-art deep neural networks for the classification of local fiber orientations.
