Search Results for author:
Found 31 papers, 16 papers with code
FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba
In this paper, we propose FusionMamba, a novel dynamic feature enhancement method for multimodal image fusion with Mamba.
3MOS: Multi-sources, Multi-resolutions, and Multi-scenes dataset for Optical-SAR image matching
Optical-SAR image matching is a fundamental task for image fusion and visual navigation.
LLM-Detector: Improving AI-Generated Chinese Text Detection with Open-Source LLM Instruction Tuning
We then proposed LLM-Detector, a novel method for both document-level and sentence-level text detection through Instruction Tuning of LLMs.
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
We use a clinical dataset with 1630 MRI scans from 314 patients treated with NAC.
Distance Guided Generative Adversarial Network for Explainable Binary Classifications
Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification.
Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-Tuning
This work is pioneering in the execution of instruction fine-tuning on a sparse expert-mixed model, marking a significant breakthrough in enhancing the capabilities of this model architecture.
Fine-Grained Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation
The domain adaptation approach has gained significant acceptance in transferring styles across various vendors and centers, along with filling the gaps in modalities.
A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets.
Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation
Inspired by semi-supervised algorithms that use both labeled and unlabeled data for training, we propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation.
Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation
In this paper, we present a novel semi-supervised learning method, Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation (DCPA), for medical image segmentation.
IvyGPT: InteractiVe Chinese pathwaY language model in medical domain
General large language models (LLMs) such as ChatGPT have shown remarkable success.
DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning
The question of 'what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?'
Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism
DWIs with different b-values are fused to efficiently utilize the difference features of DWIs.
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification
To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks.
TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution
We perform a generative task to encapsulate diverse priors into a generative network (GAN), which is the decoder sub-module of the deep non-local feature capture part, in the first stage.
Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video Enhancement
To this end, we first construct a Low-Light Video Enhancement Quality Assessment (LLVE-QA) dataset in which 254 original low-light videos are collected and then enhanced by leveraging 8 LLVE algorithms to obtain 2, 060 videos in total.
IMPORTANT-Net: Integrated MRI Multi-Parameter Reinforcement Fusion Generator with Attention Network for Synthesizing Absent Data
Then the multi-parameter fusion with attention module enables the interaction of the encoded information from different parameters through a set of algorithmic strategies, and applies different weights to the information through the attention mechanism after information fusion to obtain refined representation information.
Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI
Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences.
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset
Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.
SMOC-Net: Leveraging Camera Pose for Self-Supervised Monocular Object Pose Estimation
The teacher model contains a backbone estimation module for initial object pose estimation, and an object pose refiner for refining the initial object poses using a geometric constraint (called relative-pose constraint) derived from relative camera poses.
Network Topology Inference based on Timing Meta-Data
If the data timings and ACK timings of two nodes -- say node 1 and node 2, respectively -- are causally related, this may be taken as evidence that node 1 is communicating to node 2 (which sends back ACK packets to node 1).
Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learning
Domain adaptation has been widely adopted to transfer styles across multi-vendors and multi-centers, as well as to complement the missing modalities.
Mass Segmentation in Automated 3-D Breast Ultrasound Using Dual-Path U-net
Automated 3-D breast ultrasound (ABUS) is a newfound system for breast screening that has been proposed as a supplementary modality to mammography for breast cancer detection.
To Raise or Not To Raise: The Autonomous Learning Rate Question
There is a parameter ubiquitous throughout the deep learning world: learning rate.
Pristine annotations-based multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19
The COVID-19 pandemic continues to spread and impact the well-being of the global population.
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data
Such performance gain is more pronounced with transfer learning or in the case of limited training data.
Lesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets
The method takes the advantage of a fully convolutional neural network (FCN) and a generative adversarial net to segment a lesion by using prior knowledge.
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome.
Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning
To help doctors to be more selective on biopsies and provide a second opinion on diagnosis, in this work, we propose a computer-aided diagnosis (CAD) system for lung diseases including cancers and tuberculosis (TB).
Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network
Without noise level parameter, our general noise-applicable model is also better than the other compared methods in two datasets.
