Search Results for author:
Found 16 papers, 6 papers with code
SpineCLUE: Automatic Vertebrae Identification Using Contrastive Learning and Uncertainty Estimation
In this paper, we propose a three-stage method to address the challenges in 3D CT vertebrae identification at vertebrae-level.
Rapid Open-World Adaptation by Adaptation Principles Learning
This is an important characteristic of intelligent agents, as it allows them to continue to function effectively in novel or unexpected situations, but still stands as a critical challenge for deep reinforcement learning (DRL).
Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery
In this work, we propose a general framework that is applicable to deep reinforcement learning agents.
Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping
In this paper, we make a first attempt to explore a deep learning method for unsupervised gland segmentation, where no manual annotations are required.
Knowledge Boosting: Rethinking Medical Contrastive Vision-Language Pre-Training
To address these issues, we propose the Knowledge-Boosting Contrastive Vision-Language Pre-training framework (KoBo), which integrates clinical knowledge into the learning of vision-language semantic consistency.
NovPhy: A Testbed for Physical Reasoning in Open-world Environments
But is it enough to only have physical reasoning capabilities to operate in a real physical environment?
Don't do it: Safer Reinforcement Learning With Rule-based Guidance
During training, reinforcement learning systems interact with the world without considering the safety of their actions.
Measuring Difficulty of Novelty Reaction
In this paper, we propose to define the novelty reaction difficulty as a relative difficulty of performing the known task after the introduction of the novelty.
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training
In this paper, we propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification from noisy-labeled data to combat the lack of high quality annotated medical data.
Phy-Q as a measure for physical reasoning intelligence
Inspired by how human IQ is calculated, we define the physical reasoning quotient (Phy-Q score) that reflects the physical reasoning intelligence of an agent using the physical scenarios we considered.
Hi-Phy: A Benchmark for Hierarchical Physical Reasoning
In this paper, we propose a new benchmark for physical reasoning that allows us to test individual physical reasoning capabilities.
The Difficulty of Novelty Detection in Open-World Physical Domains: An Application to Angry Birds
Therefore, to accurately evaluate the novelty detection capability of AI systems, it is necessary to investigate how difficult it may be to detect different types of novelty.
Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation
To deal with the high inter-rater variability, the study of imperfect label has great significance in medical image segmentation tasks.
Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images
The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement.
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis
In this paper, we present a novel active learning framework for cost-effective skin lesion analysis.
Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification
In this paper, we propose an effective iterative learning framework for noisy-labeled medical image classification, to combat the lacking of high quality annotated medical data.
