Events Calendar

22 Apr
ISP AI Forum: "Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays"
Event Type

Lectures, Symposia, Etc.

Topic

Research, Technology

Target Audience

Undergraduate Students, Staff, Faculty, Graduate Students

Website

https://pitt.co1.qualtrics.com/jfe/fo...

University Unit
Intelligent Systems Program
Hashtag

#isp

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ISP AI Forum: "Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays"

This is a past event.

Abstract: The shortage of large-scale expert annotated chest X-ray datasets poses a challenge for building high precision abnormality detection models. Weakly-supervised learning (WSL) methods show significant promise to overcome this problem by leveraging information from freely available radiology reports. However, most of these methods only use image-level pathological findings, failing to utilize the information of relevant anatomy that plays an important role in radiologists' reporting process. In addition, weak labels extracted from reports are often sparse and noisy, and using a naive imputation strategy (i.e., equating \textit{no mention} to \textit{negative}) may degrade model's performance. To address these issues, we propose a novel WSL framework, anatomy-guided chest X-ray network (AGXNet), that learns the features of both radiological observations and the relevant anatomical landmarks. The key component in our framework is an anatomy-guided attention module that regularizes the feature maps learned from both anatomy and observation encoders having consistent location of abnormality. We adopt a PU learning technique to iteratively improve weak labels' qualities during training. Our quantitative and qualitative results on the MIMIC-CXR dataset demonstrate the effectiveness of AGXNet in both disease and anatomic abnormality localization. Experiments on the NIH Chest X-ray dataset demonstrate that the learned image representations are transferable and outperform the baselines in both classification and localization tasks.

Bio: Shantanu Ghosh is a PhD student in the Intelligent Systems Program.  His research interests include computer vision, causal inference, and deep learning.

RSVP for more Zoom information: https://pitt.co1.qualtrics.com/jfe/form/SV_4THGjnpJlBm5wtU 

Friday, April 22 at 1:00 p.m. to 1:30 p.m.

Virtual Event

ISP AI Forum: "Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays"

Abstract: The shortage of large-scale expert annotated chest X-ray datasets poses a challenge for building high precision abnormality detection models. Weakly-supervised learning (WSL) methods show significant promise to overcome this problem by leveraging information from freely available radiology reports. However, most of these methods only use image-level pathological findings, failing to utilize the information of relevant anatomy that plays an important role in radiologists' reporting process. In addition, weak labels extracted from reports are often sparse and noisy, and using a naive imputation strategy (i.e., equating \textit{no mention} to \textit{negative}) may degrade model's performance. To address these issues, we propose a novel WSL framework, anatomy-guided chest X-ray network (AGXNet), that learns the features of both radiological observations and the relevant anatomical landmarks. The key component in our framework is an anatomy-guided attention module that regularizes the feature maps learned from both anatomy and observation encoders having consistent location of abnormality. We adopt a PU learning technique to iteratively improve weak labels' qualities during training. Our quantitative and qualitative results on the MIMIC-CXR dataset demonstrate the effectiveness of AGXNet in both disease and anatomic abnormality localization. Experiments on the NIH Chest X-ray dataset demonstrate that the learned image representations are transferable and outperform the baselines in both classification and localization tasks.

Bio: Shantanu Ghosh is a PhD student in the Intelligent Systems Program.  His research interests include computer vision, causal inference, and deep learning.

RSVP for more Zoom information: https://pitt.co1.qualtrics.com/jfe/form/SV_4THGjnpJlBm5wtU 

Friday, April 22 at 1:00 p.m. to 1:30 p.m.

Virtual Event

University Unit
Intelligent Systems Program
Hashtag

#isp

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