ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis Using Contrastive Learning

no code implementations • 8 Mar 2024 • Chenglong Wang, Yinqiao Yi, Yida Wang, Chengxiu Zhang, Yun Liu, Kensaku MORI, Mei Yuan, Guang Yang

This framework is designed to introduce inherent transparency and provide extensive post-hoc explainability for deep learning model, making them more suitable for clinical medical diagnosis.

Contrastive Learning Medical Diagnosis

$\mathrm{SAM^{Med}}$: A medical image annotation framework based on large vision model

no code implementations • 11 Jul 2023 • Chenglong Wang, Dexuan Li, Sucheng Wang, Chengxiu Zhang, Yida Wang, Yun Liu, Guang Yang

The $\mathrm{SAM^{assist}}$ demonstrates the generalization ability of SAM to the downstream medical segmentation task using the prompt-learning approach.

Image Segmentation Liver Segmentation +3

Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule Diagnosis

no code implementations • 8 Apr 2022 • Chenglong Wang, Yun Liu, Fen Wang, Chengxiu Zhang, Yida Wang, Mei Yuan, Guang Yang

However, detection and accurate diagnosis of pulmonary nodules depend heavily on the experiences of radiologists and can be a heavy workload for them.