‘Deep Learning’

A comparison of the number of learnable parameters and the types and number of skip connections in our five models. Note that the skip connections type column lists operations which show how residual information is reintegrated when the skip / residual connection or parallel processing branch rejoins the network.

Technical Note: Comparison of Convolutional Neural Networks for Detecting Large Vessel Occlusion on Computed Tomography Angiography

Jul. 21, 2021—Lucas W. Remedios, Sneha Lingam, Samuel W. Remedios, Riqiang Gao, Stephen W. Clark, Larry T. Davis, Bennett A. Landman. Technical Note: Comparison of Convolutional Neural Networks for Detecting Large Vessel Occlusion on Computed Tomography Angiography. Medical Physics, 2021 Full Text Abstract Purpose: Artificial intelligence diagnosis and triage of large vessel occlusion may quicken clinical response for...

Learning from dispersed manual annotations with an optimized data weighting policy

Dec. 7, 2020—Yucheng Tang, Riqiang Gao, Yunqiang Chen, Dashan Gao, Michael R. Savona, Richard G. Abramson, Shunxing Bao, Yuankai Huo and Bennett A. Landman, “Learning from Dispersed Manual Annotations with an Optimized Data Weighting Policy”, Journal of Medical Imaging, 2020. Full Text: Abstract https://pubmed.ncbi.nlm.nih.gov/32775501/ Purpose: Deep learning methods have become essential tools for quantitative interpretation of medical...

Figure 1. Renal structures segmentation of clinically acquired CT image. Yellow box shows a representative right kidney, green box shows the left kidney. The zoom-in patches show the CT kidney field-of-view (FOV) and labels. The label patches display the renal cortex (blue), the medulla (red) and the pelvicalyceal system (green). Those small structures make automatic renal segmentation challenging.

Renal Cortex, Medulla and Pelvicaliceal System Segmentation on Arterial Phase CT Images with Random Patch-based Networks

Nov. 21, 2020—Yucheng Tang, Riqiang Gao, Ho Hin Lee, Brent V. Savoie, Shunxing Bao, Yuankai Huo, Jeffrey Spraggins and Bennett A, Landman, Renal Cortex, Medulla, Pelvis Segmentation on Arterial Phase CT Images with Random Patch-based Networks, SPIE 2021 Medical Imaging Full Text Abstract Renal segmentation on contrast-enhanced computed tomography (CT) provides distinct spatial context and morphology. Current studies for renal segmentations...

Montage based 3D Medical Image Retrieval from Traumatic Brain Injury Cohort using Deep Convolutional Neural Network

Dec. 10, 2018—Cailey I. Kerley, Yuankai Huo, Shikha Chaganti, Shunxing Bao, Mayur B. Patel, Bennett A. Landman. “Montage based 3D Medical Image Retrieval from Traumatic Brain Injury Cohort using Deep Convolutional Neural Network.” In SPIE Medical Imaging, International Society for Optics and Photonics, 2019. Full text: NIHMSID Abstract Brain imaging analysis on clinically acquired computed tomography (CT) is essential...