Meet ApplyKite Twin, the application assistant that learns with you

Meet your Twin, it learns with you

Your Twin learns with you

Launch week codes:30% off annual Pro20% off monthly Pro

Ends in00h00m00s

Yi Lin

Weill Cornell Medicine

United States

Research Interests

Explore related searches

Articles (20)

Collaborators (2)

Kevin Matthew Byrd

Special Volunteer

National Institutes of Health

UNITED STATES

Jinze Liu

Virginia Commonwealth University

UNITED STATES

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Yi Lin

Research Interests

Explore related searches

Articles (20)

Collaborators (2)

Kevin Matthew Byrd

Jinze Liu

Make your next move.

Yi Lin

Research Interests

Explore related searches

Articles (20)

Understanding Channel Complementarity for Multiplex Cell Segmentation

Prompt-guided foundation model tuning for pathology image classification

Learning with less supervision: A survey of label-efficient learning for medical image analysis

LLM-Driven Medical Report Generation via Communication-Efficient Heterogeneous Federated Learning

A Disease-Aware Dual-Stage Framework for Chest X-ray Report Generation

A generalizable pathology foundation model using a unified knowledge distillation pretraining framework

MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation

Multicentre MRI-based machine learning model for noninvasive prediction of pulmonary metastasis in osteosarcoma integrating intra-tumoral heterogeneity features

Overview of the CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-Ray Classification

Reinforcement learning improves LLM accuracy and reasoning in disease classification from radiology reports

Rethinking radiology AI benchmarks

Rethinking boundary detection in deep learning-based medical image segmentation

Boosting Convolution With Efficient MLP-Permutation for Volumetric Medical Image Segmentation

Merging Context Clustering With Visual State Space Models for Medical Image Segmentation

CoPA: Hierarchical Concept Prompting and Aggregating Network for Explainable Diagnosis

Corrigendum to “Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge” [Medical Image Analysis, April 2022, Volume 77, 102333] (Medical Image Analysis (2022) 77, (S1361841521003789), (10.1016/j.media.2021.102333))

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Adaptive Fusion of Deep Learning With Statistical Anatomical Knowledge for Robust Patella Segmentation From CT Images

Adversarial Medical Image With Hierarchical Feature Hiding

Aligning Medical Images with General Knowledge from Large Language Models

Collaborators (2)

Kevin Matthew Byrd

Jinze Liu

Make your next move.