130 citations · 306 across the 28 of their papers we have counts for
28 papers
CoPA: Hierarchical Concept Prompting and Aggregating Network for Explainable Diagnosis
Yiheng Dong, Yi Lin, Xin Yang
The transparency of deep learning models is essential for clinical diagnostics. Concept Bottleneck Model provides clear decision-making processes for diagnosis by transforming the…
LLM-driven Medical Report Generation via Communication-efficient Heterogeneous Federated Learning
Haoxuan Che, Haibo Jin, Zhengrui Guo +3
LLMs have demonstrated significant potential in Medical Report Generation (MRG), yet their development requires large amounts of medical image-report pairs, which are commonly scat…
Merging Context Clustering with Visual State Space Models for Medical Image Segmentation
Yun Zhu, Dong Zhang, Yi Lin +2
Medical image segmentation demands the aggregation of global and local feature representations, posing a challenge for current methodologies in handling both long-range and short-r…
Revisiting Deep Ensemble Uncertainty for Enhanced Medical Anomaly Detection
Yi Gu, Yi Lin, Kwang-Ting Cheng +1
Medical anomaly detection (AD) is crucial in pathological identification and localization. Current methods typically rely on uncertainty estimation in deep ensembles to detect anom…
Aligning Medical Images with General Knowledge from Large Language Models
Xiao Fang, Yi Lin, Dong Zhang +2
Pre-trained large vision-language models (VLMs) like CLIP have revolutionized visual representation learning using natural language as supervisions, and demonstrated promising gene…
Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification
Shuhan Li, Yi Lin, Hao Chen +1
Accurate and robust classification of diseases is important for proper diagnosis and treatment. However, medical datasets often face challenges related to limited sample sizes and…
Prompt-Guided Foundation Model Tuning for Pathology Image Classification
Yi Lin, Zhengjie Zhu, Kwang-Ting Cheng +1
Foundation models have become pivotal in advancing computational pathology, particularly for whole slide image (WSI) classification. However, prevailing methodologies often rely on…
BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels
Yi Lin, Zeyu Wang, Dong Zhang +2
Nuclei segmentation is a fundamental prerequisite in the digital pathology workflow. The development of automated methods for nuclei segmentation enables quantitative analysis of t…
Adversarial Medical Image with Hierarchical Feature Hiding
Qingsong Yao, Zecheng He, Yuexiang Li +4
Deep learning based methods for medical images can be easily compromised by adversarial examples (AEs), posing a great security flaw in clinical decision-making. It has been discov…
Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks
Xin Yang, Yi Lin, Zhiwei Wang +2
In this paper, we propose a bi-modality medical image synthesis approach based on sequential generative adversarial network (GAN) and semi-supervised learning. Our approach consist…
PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation
Haibo Jin, Haoxuan Che, Yi Lin +1
Automatic medical report generation (MRG) is of great research value as it has the potential to relieve radiologists from the heavy burden of report writing. Despite recent advance…
Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation
Yi Lin, Dong Zhang, Xiao Fang +3
Medical image segmentation is a fundamental task in the community of medical image analysis. In this paper, a novel network architecture, referred to as Convolution, Transformer, a…
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Luyang Luo, Xi Wang, Yi Lin +7
Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to imp…
Exploring Visual Prompts for Whole Slide Image Classification with Multiple Instance Learning
Yi Lin, Zhongchen Zhao, Zhengjie ZHU +3
Multiple instance learning (MIL) has emerged as a popular method for classifying histopathology whole slide images (WSIs). However, existing approaches typically rely on pre-traine…
Few Shot Medical Image Segmentation with Cross Attention Transformer
Yi Lin, Yufan Chen, Kwang-Ting Cheng +1
Medical image segmentation has made significant progress in recent years. Deep learning-based methods are recognized as data-hungry techniques, requiring large amounts of data with…
Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation
Yi Lin, Xiao Fang, Dong Zhang +2
Recently, the advent of vision Transformer (ViT) has brought substantial advancements in 3D dataset benchmarks, particularly in 3D volumetric medical image segmentation (Vol-MedSeg…
Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions
Cheng Jin, Zhengrui Guo, Yi Lin +2
Deep learning has significantly advanced medical imaging analysis (MIA), achieving state-of-the-art performance across diverse clinical tasks. However, its success largely depends…
TAKT: Target-Aware Knowledge Transfer for Whole Slide Image Classification
Conghao Xiong, Yi Lin, Hao Chen +5
Transferring knowledge from a source domain to a target domain can be crucial for whole slide image classification, since the number of samples in a dataset is often limited due to…
Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions
Dong Zhang, Yi Lin, Hao Chen +4
Over the past few years, the rapid development of deep learning technologies for computer vision has significantly improved the performance of medical image segmentation (MedISeg).…
Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression
Yi Lin, Luyan Liu, Kai Ma +1
Automated methods for Cobb angle estimation are of high demand for scoliosis assessment. Existing methods typically calculate the Cobb angle from landmark estimation, or simply com…
InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation
Yi Lin, Zeyu Wang, Kwang-Ting Cheng +1
Nuclei Segmentation from histology images is a fundamental task in digital pathology analysis. However, deep-learning-based nuclei segmentation methods often suffer from limited an…
Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs
Zhiwei Wang, Jinxin Lv, Yunqiao Yang +5
Vertebral landmark localization is a crucial step for variant spine-related clinical applications, which requires detecting the corner points of 17 vertebrae. However, the neighbor…
Deep Learning for Computational Cytology: A Survey
Hao Jiang, Yanning Zhou, Yi Lin +3
Computational cytology is a critical, rapid-developing, yet challenging topic in the field of medical image computing which analyzes the digitized cytology image by computer-aided…
Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training
Yi Lin, Zhiyong Qu, Hao Chen +6
Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on t…