Search Results for author:
Found 7 papers, 3 papers with code
Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge
The best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89. 5\%, 90. 9\%, and 88. 3\% on North American, European, and Asian cohorts, respectively.
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
Cost Splitting for Multi-Objective Conflict-Based Search
Our theoretical results show that, when combined with either of these two new splitting strategies, MO-CBS maintains its completeness and optimality guarantees.
Deep Baseline Network for Time Series Modeling and Anomaly Detection
By using this deep network, we can easily locate the baseline position and then provide reliable and interpretable anomaly detection result.
DePS: An improved deep learning model for de novo peptide sequencing
De novo peptide sequencing from mass spectrometry data is an important method for protein identification.
AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets.
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation
Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients.
