11 citations · 11 across the 10 of their papers we have counts for
10 papers
SLT: Robust Quantum Neural Networks for Noisy-Label Medical Image Classification via Supermartingale-based Label Transition
Jun Zhuang, Mohammad Al Hasan, Yiyu Shi +1
Noisy-label learning in small-scale medical image classification is challenging and hinders the superiority of deep neural networks. Recent studies suggest that quantum neural netw…
Text Over Image: Auditing Multimodal Robustness in Synthetic Medical Image Detection
Ching-Hao Chiu, Hao-Wei Chung, Gelei Xu +7
With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud. Although prior work has explored vision-l…
Contrastive Learning with Diffusion Features for Weakly Supervised Medical Image Segmentation
Dewen Zeng, Xinrong Hu, Yu-Jen Chen +3
Weakly supervised semantic segmentation (WSSS) methods using class labels often rely on class activation maps (CAMs) to localize objects. However, traditional CAM-based methods str…
Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation
Xinrong Hu, Yiyu Shi
Collecting pixel-level labels for medical datasets can be a laborious and expensive process, and enhancing segmentation performance with a scarcity of labeled data is a crucial cha…
Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature Masking
Yu-Jen Chen, Xueyang Li, Yiyu Shi +1
Out-of-distribution (OOD) detection is essential for ensuring the reliability of deep learning models in medical imaging applications. This work is motivated by the observation tha…
Enhancing 3D Transformer Segmentation Model for Medical Image with Token-level Representation Learning
Xinrong Hu, Dewen Zeng, Yawen Wu +2
In the field of medical images, although various works find Swin Transformer has promising effectiveness on pixelwise dense prediction, whether pre-training these models without us…
Contrastive Learning with Synthetic Positives
Dewen Zeng, Yawen Wu, Xinrong Hu +2
Contrastive learning with the nearest neighbor has proved to be one of the most efficient self-supervised learning (SSL) techniques by utilizing the similarity of multiple instance…
Achieving Fairness Through Channel Pruning for Dermatological Disease Diagnosis
Qingpeng Kong, Ching-Hao Chiu, Dewen Zeng +4
Numerous studies have revealed that deep learning-based medical image classification models may exhibit bias towards specific demographic attributes, such as race, gender, and age.…
Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space
Hao-Wei Chung, Ching-Hao Chiu, Yu-Jen Chen +2
Fairness has become increasingly pivotal in machine learning for high-risk applications such as machine learning in healthcare and facial recognition. However, we see the deficienc…
Achieve Fairness without Demographics for Dermatological Disease Diagnosis
Ching-Hao Chiu, Yu-Jen Chen, Yawen Wu +2
In medical image diagnosis, fairness has become increasingly crucial. Without bias mitigation, deploying unfair AI would harm the interests of the underprivileged population and po…