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Found 17 papers, 3 papers with code
Eye-gaze Guided Multi-modal Alignment Framework for Radiology
Additionally, we explore the impact of varying amounts of eye-gaze data on model performance, highlighting the feasibility and utility of integrating this auxiliary data into multi-modal pre-training.
InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning
In this paper, we introduce InterLUDE, a new approach to enhance SSL made of two parts that each benefit from labeled-unlabeled interaction.
Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis Diagnosis
However, existing deep learning pipelines for assessing AS from echocardiograms have two key limitations.
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
NoisyNN: Exploring the Influence of Information Entropy Change in Learning Systems
Herein, we use the information entropy to define the complexity of the task.
Ranked #270 on
Image Classification
on ImageNet
Robust Core-Periphery Constrained Transformer for Domain Adaptation
Unsupervised domain adaptation (UDA) aims to learn transferable representation across domains.
Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification
With the popularity of deep neural networks (DNNs), model interpretability is becoming a critical concern.
Segment Anything Model (SAM) for Radiation Oncology
Given that SAM, a model pre-trained purely on natural images, can handle the delineation of OARs from medical images with clinically acceptable accuracy, these results highlight SAM's robust generalization capabilities with consistent accuracy in automatic segmentation for radiotherapy.
Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT
Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.
Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task
To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples.
When Brain-inspired AI Meets AGI
Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do.
Core-Periphery Principle Guided Redesign of Self-Attention in Transformers
Designing more efficient, reliable, and explainable neural network architectures is critical to studies that are based on artificial intelligence (AI) techniques.
DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4
The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy.
Gyri vs. Sulci: Disentangling Brain Core-Periphery Functional Networks via Twin-Transformer
To solve this fundamental problem, we design a novel Twin-Transformer framework to unveil the unique functional roles of gyri and sulci as well as their relationship in the whole brain function.
Representing Brain Anatomical Regularity and Variability by Few-Shot Embedding
Our experimental results show that: 1) the learned embedding vectors can quantitatively encode the commonality and individuality of cortical folding patterns; 2) with the embeddings we can robustly infer the complicated many-to-many anatomical correspondences among different brains and 3) our model can be successfully transferred to new populations with very limited training samples.
Disentangling Spatial-Temporal Functional Brain Networks via Twin-Transformers
In this work, we propose a novel Twin-Transformers framework to simultaneously infer common and individual functional networks in both spatial and temporal space, in a self-supervised manner.
Representative Functional Connectivity Learning for Multiple Clinical groups in Alzheimer's Disease
By mapping the learned clinical group related feature vectors to the original FC space, representative FCs were constructed for each group.
