Thoracic Disease Identification and Localization with Limited Supervision

Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning. Building a highly accurate prediction model for these tasks usually requires a large number of images manually annotated with labels and finding sites of abnormalities. In reality, however, such annotated data are expensive to acquire, especially the ones with location annotations. We need methods that can work well with only a small amount of location annotations. To address this challenge, we present a unified approach that simultaneously performs disease identification and localization through the same underlying model for all images. We demonstrate that our approach can effectively leverage both class information as well as limited location annotation, and significantly outperforms the comparative reference baseline in both classification and localization tasks.Comment: Conference on Computer Vision and Pattern Recognition 2018 (CVPR 2018). V1: CVPR submission; V2: +supplementary; V3: CVPR camera-ready; V4: correction, update reference baseline results according to their latest post; V5: minor correction; V6: Identification results using NIH data splits and various image model

Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs

The lack of fine-grained annotations hinders the deployment of automated diagnosis systems, which require human-interpretable justification for their decision process. In this paper, we address the problem of weakly supervised identification and localization of abnormalities in chest radiographs. To that end, we introduce a novel loss function for training convolutional neural networks increasing the \emph{localization confidence} and assisting the overall \emph{disease identification}. The loss leverages both image- and patch-level predictions to generate auxiliary supervision. Rather than forming strictly binary from the predictions as done in previous loss formulations, we create targets in a more customized manner, which allows the loss to account for possible misclassification. We show that the supervision provided within the proposed learning scheme leads to better performance and more precise predictions on prevalent datasets for multiple-instance learning as well as on the NIH~ChestX-Ray14 benchmark for disease recognition than previously used losses

Risk factors for paravalvular leak after transcatheter aortic valve replacement

Objective. To assess risk factors for paravalvular leak (PVL) after transcatheter aortic valve implantation (TAVI) in a large single-center cohort, including measurement of aortic valve calcification using a reproducible method. Methods. We retrospectively analyzed preoperative contrast-enhanced multidetector computed tomography (MDCT) scans of patients who underwent TAVI in our center between 2009 and 2016. Calcium volume was calculated for each aortic cusp in the aortic valve (AV), left ventricular outflow tract (LVOT) and device-landing zone (DLZ). Results. Overall, 539 patients were included in the study (Edwards SapienXT, n=192; Edwards Sapien3, n=206; Medtronic CoreValve EvolutR, n=44; Symetis Acurate, n=97). Median calcium volume in the DLZ was 757 mm3, with no significant differences among the four prosthesis groups. None of the patients had severe PVL. The overall incidence of mild-to-moderate PVL was 15.8% (95% CI: 12.8-19.1%). On multivariate logistic regression, DLZ calcification (p=0.00006; OR for an increase of 100 mm3 1.08; 95% CI: 1.04-1.13) and use of the CoreValve (p=0.0028; OR 4.1; 95% CI: 1.6-10 with SapienXT as reference) prosthesis were found to be associated with ≥mild PVL. In contrast, degree of oversizing (p=0.002; OR 0.97; 95% CI: 0.95-0.99), and use of Sapien3 (p=0.00005; OR 0.23; 95% CI: 0.11-0.47 with SapienXT as reference) were associated with a lower incidence of ≥mild PVL. Conclusions. Aortic calcification volume in the DLZ is associated with residual PVL after TAVI. When taking calcification into account, the balloon-expandable prosthesis Sapien3 seems to be associated with a lower incidence of PVL