56 citations · 223 across the 22 of their papers we have counts for
23 papers
Improving Medical Visual Reinforcement Fine-Tuning via Perception and Reasoning Augmentation
Guangjing Yang, ZhangYuan Yu, Ziyuan Qin +7
While recent advances in Reinforcement Fine-Tuning (RFT) have shown that rule-based reward schemes can enable effective post-training for large language models, their extension to…
Confounder-Aware Medical Data Selection for Fine-Tuning Pretrained Vision Models
Anyang Ji, Qingbo Kang, Wei Xu +3
The emergence of large-scale pre-trained vision foundation models has greatly advanced the medical imaging field through the pre-training and fine-tuning paradigm. However, selecti…
Guiding Medical Vision-Language Models with Explicit Visual Prompts: Framework Design and Comprehensive Exploration of Prompt Variations
Kangyu Zhu, Ziyuan Qin, Huahui Yi +4
While mainstream vision-language models (VLMs) have advanced rapidly in understanding image level information, they still lack the ability to focus on specific areas designated by…
One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection
Yiyue Li, Shaoting Zhang, Kang Li +1
Traditional Anomaly Detection (AD) methods have predominantly relied on unsupervised learning from extensive normal data. Recent AD methods have evolved with the advent of large pr…
Evaluating Hallucination in Text-to-Image Diffusion Models with Scene-Graph based Question-Answering Agent
Ziyuan Qin, Dongjie Cheng, Haoyu Wang +5
Contemporary Text-to-Image (T2I) models frequently depend on qualitative human evaluations to assess the consistency between synthesized images and the text prompts. There is a dem…
MLAE: Masked LoRA Experts for Visual Parameter-Efficient Fine-Tuning
Junjie Wang, Guangjing Yang, Wentao Chen +4
In response to the challenges posed by the extensive parameter updates required for full fine-tuning of large-scale pre-trained models, parameter-efficient fine-tuning (PEFT) metho…
OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine
Xiaosong Wang, Xiaofan Zhang, Guotai Wang +17
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and…
TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation
Zekun Jiang, Dongjie Cheng, Ziyuan Qin +10
This study presents a novel multimodal medical image zero-shot segmentation algorithm named the text-visual-prompt segment anything model (TV-SAM) without any manual annotations. T…
Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image Recognition
Qingbo Kang, Jun Gao, Kang Li +1
Masked autoencoder (MAE) has attracted unprecedented attention and achieves remarkable performance in many vision tasks. It reconstructs random masked image patches (known as proxy…
ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models
Huahui Yi, Ziyuan Qin, Wei Xu +5
Large pre-trained vision-language models have shown great prominence in transferring pre-acquired knowledge to various domains and downstream tasks with appropriate prompting or tu…
SAM on Medical Images: A Comprehensive Study on Three Prompt Modes
Dongjie Cheng, Ziyuan Qin, Zekun Jiang +3
The Segment Anything Model (SAM) made an eye-catching debut recently and inspired many researchers to explore its potential and limitation in terms of zero-shot generalization capa…
Towards General Purpose Medical AI: Continual Learning Medical Foundation Model
Huahui Yi, Ziyuan Qin, Qicheng Lao +5
Inevitable domain and task discrepancies in real-world scenarios can impair the generalization performance of the pre-trained deep models for medical data. Therefore, we audaciousl…
Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study
Ziyuan Qin, Huahui Yi, Qicheng Lao +1
The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images. However, it remains unknown whether this capability can…
FlowX: Towards Explainable Graph Neural Networks via Message Flows
Shurui Gui, Hao Yuan, Jie Wang +3
We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms. While most current methods focus on explaining graph nodes,…
Towards Adversarial Robustness via Transductive Learning
Jiefeng Chen, Yang Guo, Xi Wu +4
There has been emerging interest to use transductive learning for adversarial robustness (Goldwasser et al., NeurIPS 2020; Wu et al., ICML 2020). Compared to traditional "test-time…
Hypothesis Disparity Regularized Mutual Information Maximization
Qicheng Lao, Xiang Jiang, Mohammad Havaei
We propose a hypothesis disparity regularized mutual information maximization~(HDMI) approach to tackle unsupervised hypothesis transfer -- as an effort towards unifying hypothesis…
Conditional Generation of Medical Images via Disentangled Adversarial Inference
Mohammad Havaei, Ximeng Mao, Yiping Wang +1
Synthetic medical image generation has a huge potential for improving healthcare through many applications, from data augmentation for training machine learning systems to preservi…
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Xiang Jiang, Qicheng Lao, Stan Matwin +1
We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution s…
Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay
Qicheng Lao, Xiang Jiang, Mohammad Havaei +1
Learning in non-stationary environments is one of the biggest challenges in machine learning. Non-stationarity can be caused by either task drift, i.e., the drift in the conditiona…
FoCL: Feature-Oriented Continual Learning for Generative Models
Qicheng Lao, Mehrzad Mortazavi, Marzieh Tahaei +3
In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL). Unlike previous works that aim to solve the c…
Dual Adversarial Inference for Text-to-Image Synthesis
Qicheng Lao, Mohammad Havaei, Ahmad Pesaranghader +3
Synthesizing images from a given text description involves engaging two types of information: the content, which includes information explicitly described in the text (e.g., color,…
Case-Based Histopathological Malignancy Diagnosis using Convolutional Neural Networks
Qicheng Lao, Thomas Fevens
In practice, histopathological diagnosis of tumor malignancy often requires a human expert to scan through histopathological images at multiple magnification levels, after which a…
Leveraging Disease Progression Learning for Medical Image Recognition
Qicheng Lao, Thomas Fevens, Boyu Wang
Unlike natural images, medical images often have intrinsic characteristics that can be leveraged for neural network learning. For example, images that belong to different stages of…