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BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once
On object recognition, which aims to identify all objects in a given image along with their semantic types, we showed that BiomedParse can simultaneously segment and label all biomedical objects in an image (all at once).
Foundation Models for Biomedical Image Segmentation: A Survey
This transformative technology, originally developed for general-purpose computer vision, has found rapid application in medical image processing.
Pareto Optimal Learning for Estimating Large Language Model Errors
We present a method based on Pareto optimization that generates a risk score to estimate the probability of error in an LLM response by integrating multiple sources of information.
Financial Time Series Analysis and Forecasting with HHT Feature Generation and Machine Learning
We present the method of complementary ensemble empirical mode decomposition (CEEMD) and Hilbert-Huang transform (HHT) for analyzing nonstationary financial time series.
Adaptive Complementary Ensemble EMD and Energy-Frequency Spectra of Cryptocurrency Prices
We study the price dynamics of cryptocurrencies using adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis.
