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Found 10 papers, 4 papers with code
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and Optimization
Target-specific peptides, such as conotoxins, exhibit exceptional binding affinity and selectivity toward ion channels and receptors.
Solving Minimum-Cost Reach Avoid using Reinforcement Learning
Current reinforcement-learning methods are unable to directly learn policies that solve the minimum cost reach-avoid problem to minimize cumulative costs subject to the constraints of reaching the goal and avoiding unsafe states, as the structure of this new optimization problem is incompatible with current methods.
Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge
Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment.
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.
