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Found 58 papers, 26 papers with code
EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery
Recently, Multimodal Large Language Models (MLLMs) have demonstrated their immense potential in computer-aided diagnosis and decision-making.
BioTD: an online database of biotoxins
Biotoxins, mainly produced by venomous animals, plants and microorganisms, exhibit high physiological activity and unique effects such as lowering blood pressure and analgesia.
Universal Domain Adaptive Object Detection via Dual Probabilistic Alignment
IDSA utilizes instance-level sampling to mine domain-shared category samples and calculates alignment weight through Gaussian distribution to conduct the domain-shared category domain alignment to address the feature heterogeneity.
SurgBox: Agent-Driven Operating Room Sandbox with Surgery Copilot
To address these cognitive challenges in surgical training and operation, we propose SurgBox, an agent-driven sandbox framework to systematically enhance the cognitive capabilities of surgeons in immersive surgical simulations.
High-Efficient Near-Field Channel Characteristics Analysis for Large-Scale MIMO Communication Systems
A comprehensive analysis is conducted to investigate the influence of the height of the BS, motion characteristics of the MR, and antenna configurations on the channel statistics.
Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Survey
In this survey, we provide a timely and focused review of the literature on trustworthy T2I DMs, covering a concise-structured taxonomy from the perspectives of property, means, benchmarks and applications.
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning
To exploit complementary information from the auxiliary modality, we propose a Cross-Modal Selective fusion (CMS-fusion) module that selectively incorporate the frequency and spatial features from the auxiliary modality to enhance the corresponding branch of the target modality.
SurgPLAN++: Universal Surgical Phase Localization Network for Online and Offline Inference
To ensure a global understanding of the surgical procedure, we devise a phase localization strategy for SurgPLAN++ to predict phase segments across the entire video through phase proposals.
EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy Labels
In this work, we present EndoOmni, the first foundation model for zero-shot cross-domain depth estimation for endoscopy.
SurgTrack: CAD-Free 3D Tracking of Real-world Surgical Instruments
Compared with 2D instrument tracking methods, 3D instrument tracking has broader value in clinical practice, but is also more challenging due to weak texture, occlusion, and lack of Computer-Aided Design (CAD) models for 3D registration.
PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery
The field of computer vision applied to videos of minimally invasive surgery is ever-growing.
NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation
To alleviate the labor-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts.
Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma Grading
Remarkably, our FoF achieves superior performance using only histopathology slides compared to existing multimodal methods.
ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention Understanding
To address these limitations in operating rooms, we propose an audio-driven surgical instrument segmentation framework, named ASI-Seg, to accurately segment the required surgical instruments by parsing the audio commands of surgeons.
ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image Segmentation
Finally, we design the Query-Decoupled Modality Decoder (QDMD) that leverages a one-to-one strategy to provide an independent decoding channel for every modality.
PANS: Probabilistic Airway Navigation System for Real-time Robust Bronchoscope Localization
To overcome these challenges, we propose a novel Probabilistic Airway Navigation System (PANS), leveraging Monte-Carlo method with pose hypotheses and likelihoods to achieve robust and real-time bronchoscope localization.
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems
Previous works on adversarial attacks have primarily focused on white-box attacks that directly perturb the states or actions of victim agents, often in scenarios with a limited number of attacks.
EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy
While researchers have discussed the challenges of low-light enhancement in WCE, the issue of correcting for different exposure levels remains underexplored.
Transforming Surgical Interventions with Embodied Intelligence for Ultrasound Robotics
Ultrasonography has revolutionized non-invasive diagnostic methodologies, significantly enhancing patient outcomes across various medical domains.
U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation
We further delved into the potential of U-KAN as an alternative U-Net noise predictor in diffusion models, demonstrating its applicability in generating task-oriented model architectures.
A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation
Specifically, in the information reconstruction stage, we design a new user-level SFT task for collaborative information injection with the assistance of a pre-trained SRS model, which is more efficient and compatible with limited text information.
E$^3$-Net: Efficient E(3)-Equivariant Normal Estimation Network
To address this issue, we propose E3-Net to achieve equivariance for normal estimation.
VS-Assistant: Versatile Surgery Assistant on the Demand of Surgeons
In this work, by leveraging advanced multimodal large language models (MLLMs), we propose a Versatile Surgery Assistant (VS-Assistant) that can accurately understand the surgeon's intention and complete a series of surgical understanding tasks, e. g., surgical scene analysis, surgical instrument detection, and segmentation on demand.
Enhancing Surgical Robots with Embodied Intelligence for Autonomous Ultrasound Scanning
Ultrasound robots are increasingly used in medical diagnostics and early disease screening.
Transitive Vision-Language Prompt Learning for Domain Generalization
The vision-language pre-training has enabled deep models to make a huge step forward in generalizing across unseen domains.
Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
Besides, by personalized integration of domain features from other domains for each user and the innovation in the training mode, the DFEI framework can yield more accurate conversion identification.
Unified Multi-modal Diagnostic Framework with Reconstruction Pre-training and Heterogeneity-combat Tuning
Specifically, to enhance the representation abilities of vision and language encoders, we propose the Multi-level Reconstruction Pre-training (MR-Pretrain) strategy, including a feature-level and data-level reconstruction, which guides models to capture the semantic information from masked inputs of different modalities.
Unsupervised Learning for Joint Beamforming Design in RIS-aided ISAC Systems
It is critical to design efficient beamforming in reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems for enhancing spectrum utilization.
Endora: Video Generation Models as Endoscopy Simulators
In a nutshell, Endora marks a notable breakthrough in the deployment of generative AI for clinical endoscopy research, setting a substantial stage for further advances in medical content generation.
GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping
In particular, we propose an Efficient Feature Distillation (EFD) module that employs contrastive learning to efficiently and accurately distill language embeddings derived from foundational models.
UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images
Moreover, to unleash the generalization capability of SAM across a variety of nuclei images, we devise a Domain-adaptive Tuning Encoder (DT-Encoder) to seamlessly harmonize visual features with domain-common and domain-specific knowledge, and further devise a Domain Query-enhanced Decoder (DQ-Decoder) by leveraging learnable domain queries for segmentation decoding in different nuclei domains.
Exploiting Duality in Open Information Extraction with Predicate Prompt
Open information extraction (OpenIE) aims to extract the schema-free triplets in the form of (\emph{subject}, \emph{predicate}, \emph{object}) from a given sentence.
Energy Efficiency Optimization in Active Reconfigurable Intelligent Surface-Aided Integrated Sensing and Communication Systems
Energy efficiency (EE) is a challenging task in integrated sensing and communication (ISAC) systems, where high spectral efficiency and low energy consumption appear as conflicting requirements.
Surgical Temporal Action-aware Network with Sequence Regularization for Phase Recognition
Specifically, we propose an efficient multi-scale surgical temporal action (MS-STA) module, which integrates visual features with spatial and temporal knowledge of surgical actions at the cost of 2D networks.
PWISeg: Point-based Weakly-supervised Instance Segmentation for Surgical Instruments
To address this issue, we propose a novel yet effective weakly-supervised surgical instrument instance segmentation approach, named Point-based Weakly-supervised Instance Segmentation (PWISeg).
SurgPLAN: Surgical Phase Localization Network for Phase Recognition
To address these two challenges, we propose a Surgical Phase LocAlization Network, named SurgPLAN, to facilitate a more accurate and stable surgical phase recognition with the principle of temporal detection.
Weakly Supervised YOLO Network for Surgical Instrument Localization in Endoscopic Videos
In minimally invasive surgery, surgical instrument localization is a crucial task for endoscopic videos, which enables various applications for improving surgical outcomes.
Real-time Monocular Depth Estimation on Embedded Systems
Depth sensing is of paramount importance for unmanned aerial and autonomous vehicles.
PL-UNeXt: Per-stage Edge Detail and Line Feature Guided Segmentation for Power Line Detection
Power line detection is a critical inspection task for electricity companies and is also useful in avoiding drone obstacles.
Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systems
We first argue that the MSE minimization approach is equivalent to a conditional entropy learning problem, and then propose a mutual information learning formulation for solving regression problems by using a reparameterization technique.
Improving Continual Relation Extraction through Prototypical Contrastive Learning
Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks.
Neural Volumetric Mesh Generator
We first propose a diffusion-based generative model to tackle this problem by generating voxelized shapes with close-to-reality outlines and structures.
Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems
Deep learning systems have been reported to acheive state-of-the-art performances in many applications, and one of the keys for achieving this is the existence of well trained classifiers on benchmark datasets which can be used as backbone feature extractors in downstream tasks.
Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems
Deep learning systems have been reported to achieve state-of-the-art performances in many applications, and a key is the existence of well trained classifiers on benchmark datasets.
Robust Modeling of Unknown Dynamical Systems via Ensemble Averaged Learning
In the proposed ensemble averaging method, multiple models are independently trained and model predictions are averaged at each time step.
Exploring Gradient Flow Based Saliency for DNN Model Compression
Model pruning aims to reduce the deep neural network (DNN) model size or computational overhead.
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization
Matrix multiplication is the bedrock in Deep Learning inference application.
Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning
To address this problem, we propose a personalized retrogress-resilient framework to produce a superior personalized model for each client.
Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space
Consequently, a trained DNN defines a predictive model for the underlying unknown PDE over structureless grids.
Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion.
Socially-Aware Conference Participant Recommendation with Personality Traits
As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants.
Data-driven learning of non-autonomous systems
To circumvent the difficulty presented by the non-autonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances.
Overcoming information reduced data and experimentally uncertain parameters in ptychography with regularized optimization
The overdetermination of the mathematical problem underlying ptychography is reduced by a host of experimentally more desirable settings.
Methods to Recover Unknown Processes in Partial Differential Equations Using Data
Various numerical examples are then presented to demonstrate the performance and properties of the numerical methods.
On generalized residue network for deep learning of unknown dynamical systems
When an existing coarse model is not available, we present numerical strategies for fast creation of coarse models, to be used in conjunction with the generalized ResNet.
Complementary Network with Adaptive Receptive Fields for Melanoma Segmentation
To tackle these issues, we propose a novel complementary network with adaptive receptive filed learning.
Parameterized Synthetic Image Data Set for Fisheye Lens
Based on different projection geometry, a fisheye image can be presented as a parameterized non-rectilinear image.
Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification
In this work, we present a deep learning framework for multi-class breast cancer image classification as our submission to the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology images (BACH).
Breast Cancer Histology Image Classification
General Classification
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