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20182025
most citedWhat evidence does deep learning model use to classify Skin Lesions?

8 citations · 36 across the 28 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CL2024

MultiViewDx: Evidence-Linked Multi-View Clinical Diagnosis

Junda Wang, Yujan Ting, Zonghai Yao +5

Medical multimodal large language models (MLLMs) can perform well on existing medical visual question answering (MedVQA) benchmarks, but their training data often does not match cl…

eess.IV2024

Auxiliary Input in Training: Incorporating Catheter Features into Deep Learning Models for ECG-Free Dynamic Coronary Roadmapping

Yikang Liu, Lin Zhao, Eric Z. Chen +3

Dynamic coronary roadmapping is a technology that overlays the vessel maps (the "roadmap") extracted from an offline image sequence of X-ray angiography onto a live stream of X-ray…

cs.CV2024★ 1 cited

Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models

Lin Zhao, Xiao Chen, Eric Z. Chen +3

Medical image segmentation is crucial for clinical decision-making, but the scarcity of annotated data presents significant challenges. Few-shot segmentation (FSS) methods show pro…

cs.CV2024

Federated Data Model

Xiao Chen, Shunan Zhang, Eric Z. Chen +4

In artificial intelligence (AI), especially deep learning, data diversity and volume play a pivotal role in model development. However, training a robust deep learning model often…

eess.IV2024★ 1 cited

Spatiotemporal Diffusion Model with Paired Sampling for Accelerated Cardiac Cine MRI

Shihan Qiu, Shaoyan Pan, Yikang Liu +7

Current deep learning reconstruction for accelerated cardiac cine MRI suffers from spatial and temporal blurring. We aim to improve image sharpness and motion delineation for cine…

eess.IV2024

Clinically Feasible Diffusion Reconstruction for Highly-Accelerated Cardiac Cine MRI

Shihan Qiu, Shaoyan Pan, Yikang Liu +7

The currently limited quality of accelerated cardiac cine reconstruction may potentially be improved by the emerging diffusion models, but the clinically unacceptable long processi…