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Found 18 papers, 5 papers with code
Cross-modal Contrastive Attention Model for Medical Report Generation
Medical report automatic generation has gained increasing interest recently as a way to help radiologists write reports more efficiently.
MEPNet: Medical Entity-balanced Prompting Network for Brain CT Report Generation
By introducing the visual embedding and the learning status of medical entities as enriched clues, our method prompts the LLM to balance the learning of diverse entities, thereby enhancing reports with comprehensive findings.
A New Federated Learning Framework Against Gradient Inversion Attacks
By revisiting the key to privacy exposure in FL under GIA, which lies in the frequent sharing of model gradients that contain private data, we take a new perspective by designing a novel privacy preserve FL framework that effectively ``breaks the direct connection'' between the shared parameters and the local private data to defend against GIA.
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution
In practice, inspired by the fact that image super-resolution and segmentation can benefit each other, we propose SegSR which introduces a dual-diffusion framework to facilitate interaction between the image super-resolution and segmentation diffusion models.
See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning
2) Shifted semantic representing: Limited medical corpus causes difficulties for models to transfer the learned textual representations to generative layers.
Augmented Risk Prediction for the Onset of Alzheimer's Disease from Electronic Health Records with Large Language Models
Specifically, we develop a collaborative pipeline that combines SLs and LLMs via a confidence-driven decision-making mechanism, leveraging the strengths of SLs in clear-cut cases and LLMs in more complex scenarios.
Can LLMs Deeply Detect Complex Malicious Queries? A Framework for Jailbreaking via Obfuscating Intent
To demonstrate and address the underlying maliciousness, we propose a theoretical hypothesis and analytical approach, and introduce a new black-box jailbreak attack methodology named IntentObfuscator, exploiting this identified flaw by obfuscating the true intentions behind user prompts. This approach compels LLMs to inadvertently generate restricted content, bypassing their built-in content security measures.
Large Language Models in Medical Term Classification and Unexpected Misalignment Between Response and Reasoning
This study assesses the ability of state-of-the-art large language models (LLMs) including GPT-3. 5, GPT-4, Falcon, and LLaMA 2 to identify patients with mild cognitive impairment (MCI) from discharge summaries and examines instances where the models' responses were misaligned with their reasoning.
General Phrase Debiaser: Debiasing Masked Language Models at a Multi-Token Level
The social biases and unwelcome stereotypes revealed by pretrained language models are becoming obstacles to their application.
Generic Attention-model Explainability by Weighted Relevance Accumulation
In this paper, we propose a weighted relevancy strategy, which takes the importance of token values into consideration, to reduce distortion when equally accumulating relevance.
Pay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysis
Based on EmoGraph2vec model, we design a novel neural network to incorporate text and emoji information into sentiment analysis, which uses a hybrid-attention module combined with TextCNN-based classifier to improve performance.
Emoji-based Co-attention Network for Microblog Sentiment Analysis
In this paper, we propose an emoji-based co-attention network that learns the mutual emotional semantics between text and emojis on microblogs.
Multi Point-Voxel Convolution (MPVConv) for Deep Learning on Point Clouds
Extensive experiments on benchmark datasets such as ShapeNet Part, S3DIS and KITTI for various tasks show that MPVConv improves the accuracy of the backbone (PointNet) by up to \textbf{36\%}, and achieves higher accuracy than the voxel-based model with up to \textbf{34}$\times$ speedups.
Multi Voxel-Point Neurons Convolution (MVPConv) for Fast and Accurate 3D Deep Learning
Extensive experiments on benchmark datasets such as ShapeNet Part, S3DIS and KITTI for various tasks show that MVPConv improves the accuracy of the backbone (PointNet) by up to 36%, and achieves higher accuracy than the voxel-based model with up to 34 times speedup.
Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis
Furthermore, we design a Sentiment-based Rethinking mechanism (SR) by refining the HIN with sentiment label information to learn a more sentiment-aware document representation.
DyHGCN: A Dynamic Heterogeneous Graph Convolutional Network to Learn Users' Dynamic Preferences for Information Diffusion Prediction
For one thing, previous works cannot jointly utilize both the social network and diffusion graph for prediction, which is insufficient to model the complexity of the diffusion process and results in unsatisfactory prediction performance.
A Gated Peripheral-Foveal Convolutional Neural Network for Unified Image Aesthetic Prediction
The former aims to mimic the functions of peripheral vision to encode the holistic information and provide the attended regions.
Delving Into Salient Object Subitizing and Detection
Experiments show that the proposed multi-task network outperforms existing multi-task architectures, and the auxiliary subitizing network provides strong guidance to salient object detection by reducing false positives and producing coherent saliency maps.
