UMS-Rep: Unified modality-specific representation for efficient medical image analysis
Medical image analysis typically includes several tasks such as enhancement, segmentation, and classification. Traditionally, these tasks are implemented using separate deep learning models for separate tasks, which is not efficient because it involves unnecessary training repetitions, demands great...
Main Authors: | Ghada Zamzmi, Sivaramakrishnan Rajaraman, Sameer Antani |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-01-01
|
Series: | Informatics in Medicine Unlocked |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914821000617 |
Similar Items
-
Accelerating Super-Resolution and Visual Task Analysis in Medical Images
by: Ghada Zamzmi, et al.
Published: (2020-06-01) -
Uncertainty Quantification in Segmenting Tuberculosis-Consistent Findings in Frontal Chest X-rays
by: Sivaramakrishnan Rajaraman, et al.
Published: (2022-06-01) -
Assessing the Impact of Image Resolution on Deep Learning for TB Lesion Segmentation on Frontal Chest X-rays
by: Sivaramakrishnan Rajaraman, et al.
Published: (2023-02-01) -
Modality-Specific Deep Learning Model Ensembles Toward Improving TB Detection in Chest Radiographs
by: Sivaramakrishnan Rajaraman, et al.
Published: (2020-01-01) -
Trilateral Attention Network for Real-Time Cardiac Region Segmentation
by: Ghada Zamzmi, et al.
Published: (2021-01-01)