One paper accepted at IPMI 2021.

Congratulations on Tom van Sonsbeek’s paper “Variational Knowledge Distillation for Disease Classification in Chest X-Rays” being accepted by IPMI 2021.

Disease classification relying solely on imaging data attracts great interest in medical image analysis. Current models could be fur- ther improved, however, by also employing Electronic Health Records (EHRs), which contain rich information on patients and findings from clinicians. It is challenging to incorporate this information into disease classification due to the high reliance on clinician input in EHRs, lim- iting the possibility for automated diagnosis. In this paper, we propose variational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays that lever- ages knowledge from EHRs. Specifically, we introduce a conditional la- tent variable model, where we infer the latent representation of the X-ray image with the variational posterior conditioning on the associated EHR text. By doing so, the model acquires the ability to extract the visual features relevant to the disease during learning and can therefore perform more accurate classification for unseen patients at inference based solely on their X-ray scans. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs. The results show that the proposed variational knowledge distillation can consistently improve the performance of medical image classification and significantly surpasses current methods.

Cees G.M. Snoek
Cees G.M. Snoek