Author

Nucleus subtype classification using inter-modality learning

Dec. 19, 2023—Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios, Ho Hin Lee, Leon Y. Cai, Thomas Li, Ruining Deng, Can Cui, Jia Li, Qi Liu, Ken S. Lau, Joseph T. Roland, Mary K. Washington, Lori A. Coburn, Keith T. Wilson, Yuankai Huo, Bennett A. Landman (2024). Nucleus subtype classification using inter-modality learning. SPIE Medical Imaging 2024 :...

Exploring shared memory architectures for end-to-end gigapixel deep learning

Dec. 19, 2023—Lucas W. Remedios, Leon Y. Cai, Samuel W. Remedios, Karthik Ramadass, Aravind Krishnan, Ruining Deng, Can Cui, Shunxing Bao, Lori A. Coburn, Yuankai Huo, Bennett A. Landman (2023). Exploring shared memory architectures for end-to-end gigapixel deep learning. MIDL 2023 short paper track Full text: NIHMSID Abstract Deep learning has made great strides in medical imaging, enabled by hardware advances in GPUs. One major constraint for the development...

UMAP of the normalized histogram representation of WSI neutrophil density for the entire dataset. All patches in this figure depict the 256 × 256 pixel CLAM patch with the maximum neutrophil density from the corresponding WSI. The UMAP displayed with patches depicts more clearly visible crypts and tissue edges in the normal cluster max patches than the active disease cluster (A). The UMAP displayed with circle markers highlights the separability of the two classes (B). Example patches are shown with their predicted neutrophil segmentation in green (C, D, E, F). Looking deep into the normal cluster, expected patches (C) and active disease outliers (D) show tissue edges and few segmented neutrophils. In the active disease cluster, expected patches (E) and normal tissue CD outliers (F) both show a larger number of segmented neutrophils, with fewer tissue edges visible (F).

Predicting Crohn’s disease severity in the colon using mixed cell nucleus density from pseudo labels

Dec. 1, 2022—Lucas W. Remedios, Shunxing Bao, Cailey I. Kerley, Leon Y. Cai, François Rheault, Ruining Deng, Can Cui, Sophie Chiron, Ken S. Lau, Joseph T. Roland, Mary K. Washington, Lori A. Coburn, Keith T. Wilson, Yuankai Huo, Bennett A. Landman (2023). Predicting Crohn’s disease severity in the colon using mixed cell nucleus density from pseudo labels....

Each image volume from the mixed CT dataset is windowed for blood (width 130 Hounsfield units, level 50 Hounsfield
units), converted into a 2D montage from resampled axial slices and normalized. A multitask deep learning model then
simultaneously performs two classifications: CT vs CTA and artifact vs no artifact.

Efficient Quality Control with Mixed CT and CTA Datasets

Dec. 10, 2021—Lucas W. Remedios, Leon Y. Cai, Colin B. Hansen, Samuel W. Remedios, Bennett A. Landman (2022). Efficient Quality Control with Mixed CT and CTA Datasets. Proc SPIE Int Soc Opt Eng. 2022. Abstract Deep learning promises the extraction of valuable information from traumatic brain injury (TBI) datasets and depends on efficient navigation when using large-scale mixed computed tomography (CT) datasets from...

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...