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Found 36 papers, 7 papers with code
GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting
3D editing plays a crucial role in many areas such as gaming and virtual reality.
IT3D: Improved Text-to-3D Generation with Explicit View Synthesis
Recent strides in Text-to-3D techniques have been propelled by distilling knowledge from powerful large text-to-image diffusion models (LDMs).
DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation
Our method jointly learns the treatment and response functions in the entire sample space to avoid treatment bias and employs an intermediate pseudo treatment effect prediction network to relieve sample imbalance.
Polyp-SAM: Transfer SAM for Polyp Segmentation
In this study, we propose Poly-SAM, a finetuned SAM model for polyp segmentation, and compare its performance to several state-of-the-art polyp segmentation models.
DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction
In this paper, we propose a Deep Multi-behavior Graph Networks (DMBGN) to shed light on this field for the voucher redemption rate prediction.
Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model
The proposed model consists of two processes: a forward process which adds Gaussian noise to real CT scans, and a reverse process in which a shifted-window transformer V-net (Swin-Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise-free CT scans.
Deep Learning in Medical Image Registration: A Review
Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.
Machine Learning in Quantitative PET Imaging
This paper reviewed the machine learning-based studies for quantitative positron emission tomography (PET).
Deep Learning in Multi-organ Segmentation
This paper presents a review of deep learning (DL) in multi-organ segmentation.
TRRNet: Tiered Relation Reasoning for Compositional Visual Question Answering
Compositional visual question answering requires reasoning over both semantic and geometry object relations.
Any-k: Anytime Top-k Tree Pattern Retrieval in Labeled Graphs
We therefore propose the novel notion of an any-k ranking algorithm: for a given time budget, re- turn as many of the top-ranked results as possible.
Social and Information Networks Databases Data Structures and Algorithms
Synthetic MRI-aided Head-and-Neck Organs-at-Risk Auto-Delineation for CBCT-guided Adaptive Radiotherapy
The proposed method was evaluated on a cohort of 65 HN cancer patients.
Medical Physics Image and Video Processing
Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review
We summarize the latest AI-based methods for tumor subregion analysis and their applications.
Self-Training Vision Language BERTs with a Unified Conditional Model
We use the labeled image data to train a teacher model and use the trained model to generate pseudo captions on unlabeled image data.
Deep Q-learning of global optimizer of multiply model parameters for viscoelastic imaging
Significance: DQMP method is promising for imaging of multiple parameters, and can be generalized to global optimization for many other complex nonconvex functions and imaging of physical parameters.
Multi-organ Segmentation Network with Adversarial Performance Validator
The proposed network organically converts the 2D-coarse result to 3D high-quality segmentation masks in a coarse-to-fine manner, allowing joint optimization to improve segmentation accuracy.
Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions
Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are scarce.
Deformable Image Registration using Unsupervised Deep Learning for CBCT-guided Abdominal Radiotherapy
The STN consists of a global generative adversarial network (GlobalGAN) and a local GAN (LocalGAN) to predict the coarse- and fine-scale motions, respectively.
Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory
We further tested the proposed model on 69 landmarks from the testing dataset that has a similar image pattern to the training pattern, resulting in a mean tracking error of 0. 94+/-0. 83 mm.
Effective End-to-End Vision Language Pretraining with Semantic Visual Loss
Current vision language pretraining models are dominated by methods using region visual features extracted from object detectors.
Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation
In this work, we propose an adversarial attack-based data augmentation method to improve the deep-learning-based segmentation algorithm for the delineation of Organs-At-Risk (OAR) in abdominal Computed Tomography (CT) to facilitate radiation therapy.
Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT
In this paper, we aimed to provide a review and tutorial for researchers in the field of medical imaging using language models to improve their tasks at hand.
SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model
Skin cancer is a prevalent and potentially fatal disease that requires accurate and efficient diagnosis and treatment.
Cross-Shaped Windows Transformer with Self-supervised Pretraining for Clinically Significant Prostate Cancer Detection in Bi-parametric MRI
We introduce a novel end-to-end Cross-Shaped windows (CSwin) transformer UNet model, CSwin UNet, to detect clinically significant prostate cancer (csPCa) in prostate bi-parametric MR imaging (bpMRI) and demonstrate the effectiveness of our proposed self-supervised pre-training framework.
Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis
The two DDPMs exchange random latent noise in the reverse processes, which helps to regularize both DDPMs and generate matching images in two modalities.
BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images
We conducted a thorough investigation of the Segment Anything Model (SAM) for the task of interactive segmentation of breast tumors in ultrasound images.
Attention-Driven Lightweight Model for Pigmented Skin Lesion Detection
The model also incorporates a knowledge-based loss weighting technique, which assigns different weights to the loss function at the class level and the instance level, helping the model focus on minority classes and challenging samples.
Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency Model
One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure.
Towards General and Efficient Online Tuning for Spark
The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance.
MAGI: Multi-Annotated Explanation-Guided Learning
This technique aims to improve the predictability of the model by incorporating human understanding of the prediction process into the training phase.
Visual Attention-Prompted Prediction and Learning
To tackle these challenges, we propose a novel framework called Visual Attention-Prompted Prediction and Learning, which seamlessly integrates visual attention prompts into the model's decision-making process and adapts to images both with and without attention prompts for prediction.
Learn to Optimize Denoising Scores for 3D Generation: A Unified and Improved Diffusion Prior on NeRF and 3D Gaussian Splatting
We propose a unified framework aimed at enhancing the diffusion priors for 3D generation tasks.
Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior
In this paper, we present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model.
DUE: Dynamic Uncertainty-Aware Explanation Supervision via 3D Imputation
Explanation supervision aims to enhance deep learning models by integrating additional signals to guide the generation of model explanations, showcasing notable improvements in both the predictability and explainability of the model.
A General and Efficient Federated Split Learning with Pre-trained Image Transformers for Heterogeneous Data
Federated Split Learning (FSL) is a promising distributed learning paradigm in practice, which gathers the strengths of both Federated Learning (FL) and Split Learning (SL) paradigms, to ensure model privacy while diminishing the resource overhead of each client, especially on large transformer models in a resource-constrained environment, e. g., Internet of Things (IoT).
Magic-Boost: Boost 3D Generation with Mutli-View Conditioned Diffusion
Benefiting from the rapid development of 2D diffusion models, 3D content creation has made significant progress recently.
