Search Results for author:
Found 17 papers, 8 papers with code
Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients
Medication recommendation systems have gained significant attention in healthcare as a means of providing tailored and effective drug combinations based on patients' clinical information.
An Incremental Update Framework for Online Recommenders with Data-Driven Prior
To address the aforementioned issue, we propose an incremental update framework for online recommenders with Data-Driven Prior (DDP), which is composed of Feature Prior (FP) and Model Prior (MP).
CLIP in Medical Imaging: A Comprehensive Survey
With the aim of facilitating a deeper understanding of this promising direction, this survey offers an in-depth exploration of the CLIP paradigm within the domain of medical imaging, regarding both refined CLIP pre-training and CLIP-driven applications.
Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis
Accordingly, we introduce the Medical contrastive Gaze Image Pre-training (McGIP) as a plug-and-play module for contrastive learning frameworks.
MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis
By fixing the weight of ViT models and only adding small low-rank plug-ins, we achieve competitive results on various diagnosis tasks across different imaging modalities using only a few trainable parameters.
Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt
Federated Learning (FL) is often impeded by communication overhead issues.
Federated PAC-Bayesian Learning on Non-IID data
Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while introducing their theorems, but few considering the non-IID challenges in FL.
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy
In response, this paper introduces AQUILA (adaptive quantization in device selection strategy), a novel adaptive framework devised to effectively handle these issues, enhancing the efficiency and robustness of FL.
Evaluating Large Language Models for Radiology Natural Language Processing
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP).
ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs
The integration of Computer-Aided Diagnosis (CAD) with Large Language Models (LLMs) presents a promising frontier in clinical applications, notably in automating diagnostic processes akin to those performed by radiologists and providing consultations similar to a virtual family doctor.
DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task
The recent progress of large language models (LLMs), including ChatGPT and GPT-4, in comprehending and responding to human instructions has been remarkable.
ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models
Large language models (LLMs) have recently demonstrated their potential in clinical applications, providing valuable medical knowledge and advice.
Deep Leakage from Model in Federated Learning
In this paper, we present two novel frameworks to demonstrate that transmitting model weights is also likely to leak private local data of clients, i. e., (DLM and DLM+), under the FL scenario.
The Spike Gating Flow: A Hierarchical Structure Based Spiking Neural Network for Online Gesture Recognition
At last, we conclude the few-shot learning paradigm of the developed network: 1) a hierarchical structure-based network design involves human prior knowledge; 2) SNNs for content based global dynamic feature detection.
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications
It is demonstrated that SAFARI under unreliable communications is guaranteed to converge at the same rate as the standard FedAvg with perfect communications.
Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks
Our experiments show that GS patterns consistently make better trade-offs between accuracy and computation efficiency compared to conventional structured sparse patterns.
Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
To sufficiently exploit such important information for recommendation, it is essential to disentangle the benign popularity bias caused by item quality from the harmful popularity bias caused by conformity.
