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Found 16 papers, 12 papers with code
AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking
AbdomenAtlas provides 673K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms.
Quality Sentinel: Estimating Label Quality and Errors in Medical Segmentation Datasets
To fill in this bridge, we introduce a regression model, Quality Sentinel, to estimate label quality compared with manual annotations in medical segmentation datasets.
Multi-Scale Feature Fusion Quantum Depthwise Convolutional Neural Networks for Text Classification
We also show the advantages of our quantum model over its classical counterparts in its ability to improve test accuracy using fewer parameters.
A Novel Image Classification Framework Based on Variational Quantum Algorithms
To overcome this limitation, we introduce a novel image classification framework that leverages variational quantum algorithms (VQAs)-hybrid approaches combining quantum and classical computing paradigms within quantum machine learning.
Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
By fine-tuning CLIP on HPD v2, we obtain Human Preference Score v2 (HPS v2), a scoring model that can more accurately predict human preferences on generated images.
X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models
This paper introduces a novel explainable image quality evaluation approach called X-IQE, which leverages visual large language models (LLMs) to evaluate text-to-image generation methods by generating textual explanations.
Label-Free Liver Tumor Segmentation
We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans.
Ranked #1 on
Tumor Segmentation
on LiTS17
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning
In this work, we pre-train DNNs on ultrasound (US) domains instead of ImageNet to reduce the domain gap in medical US applications.
Rethinking Two Consensuses of the Transferability in Deep Learning
Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans.
Synthetic Tumors Make AI Segment Tumors Better
We develop a novel strategy to generate synthetic tumors.
HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining
This work proposes a hierarchical contrastive learning (HiCo) method to improve the transferability for the US video model pretraining.
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging
In this work, we propose a meta-learning-based LR tuner, named MetaLR, to make different layers automatically co-adapt to downstream tasks based on their transferabilities across domains.
QDCNN: Quantum Dilated Convolutional Neural Network
In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest.
USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning
To alleviate this problem, an US dataset named US-4 is constructed for direct pretraining on the same domain.
When Person Re-identification Meets Changing Clothes
We find that changing clothes makes ReID a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous ReID models to identify persons with unseen (new) clothes.
Probabilistic Forecasting with Temporal Convolutional Neural Network
We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting.
Multivariate Time Series Forecasting
Probabilistic Time Series Forecasting
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