Search Results for author:
Found 22 papers, 9 papers with code
Unveiling the Generalization Power of Fine-Tuned Large Language Models
While Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, fine-tuning these models on downstream, domain-specific datasets is often necessary to yield superior performance on test sets compared to their counterparts without fine-tuning.
An Autonomous Large Language Model Agent for Chemical Literature Data Mining
The proposed approach marks a significant advancement in automating chemical literature extraction and demonstrates the potential for AI to revolutionize data management and utilization in chemistry.
A Survey of Reasoning with Foundation Models
Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation.
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis
Recent AI research plots a promising future of automatic chemical reactions within the chemistry society.
DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images
Based on this estimated discrepancy, a dynamic learning rate adjustment strategy is then developed to achieve a suitable degree of adaptation for each test sample.
Histopathological Image Classification
Image Classification
+2
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction
Precise congestion prediction from a placement solution plays a crucial role in circuit placement.
Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems.
Ranked #1 on
Surgical phase recognition
on HeiChole Benchmark
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders.
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.
Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery
Automatic surgical gesture recognition is fundamentally important to enable intelligent cognitive assistance in robotic surgery.
Ranked #1 on
Action Segmentation
on JIGSAWS
Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data.
MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data
However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training.
Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation
In this work, we present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt a segmentation network to an unlabeled target domain.
Unpaired Multi-modal Segmentation via Knowledge Distillation
We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy.
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.
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
Ranked #1 on
Liver Segmentation
on LiTS2017
(Dice metric)
Automatic Liver And Tumor Segmentation
Image Segmentation
+4
Learning to Aggregate Ordinal Labels by Maximizing Separating Width
While crowdsourcing has been a cost and time efficient method to label massive samples, one critical issue is quality control, for which the key challenge is to infer the ground truth from noisy or even adversarial data by various users.
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge
We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set.
Online Prediction of Dyadic Data with Heterogeneous Matrix Factorization
We further develop a novel online learning approach for Variational inference and use it for the online learning of HeMF, which can efficiently cope with the important large-scale DDP problem.
An Efficient Statistical Method for Image Noise Level Estimation
In this paper, we address the problem of estimating noise level from a single image contaminated by additive zero-mean Gaussian noise.
