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ORCIDDigital and Computational Pathology 2022: San Diego, CA, USA
- John E. Tomaszewski, Aaron D. Ward, Richard M. Levenson:
Medical Imaging 2022: Digital and Computational Pathology, San Diego, CA, USA, February 20-24, 2022. SPIE Proceedings 12039, SPIE 2022, ISBN 9781510649538 - Front Matter: Volume 12039.
Monday morning Keynotes
- Faisal Mahmood:
Data-efficient and multimodal computational pathology.
Grading and Classification of Pathology Images
- Farzad Fereidouni, Taryn Morningstar, Alexander Borowsky, Richard M. Levenson:
FIBI: a direct-to-digital microscopy approach for slide-free histology. - Chuheng Chen, Cheng Lu
, Joseph E. Willis, Anant Madabhushi:
Identifying the origination of liver metastasis using a hand-crafted computational pathology approach. - Jun Li, Zhiguo Jiang, Yushan Zheng, Haopeng Zhang, Jun Shi, Dingyi Hu, Wei Luo, Zhongmin Jiang, Chenghai Xue:
Weakly supervised histopathological image representation learning based on contrastive dynamic clustering. - Jennifer Salguero López, Prateek Prasanna, Germán Corredor, Angel Cruz-Roa, David Becerra, Eduardo Romero:
Selecting training samples for ovarian cancer classification via a semi-supervised clustering approach. - Rakesh Choudhary, Dhadma Balachandran, Jonathan Folmsbee, Jawaria Rahman, Margaret Brandwein-Weber, Scott Doyle:
Automatic flagging of AI segmentation errors in computational pathology. - Kristyna Manousková, Valentin Abadie, Mehdi Ounissi, Gabriel Jimenez, Lev Stimmer, Benoît Delatour, Stanley Durrleman, Daniel Racoceanu:
Tau protein discrete aggregates in Alzheimer's disease: neuritic plaques and tangles detection and segmentation using computational histopathology.
Automated Quantification of tissue biomarkers
- Thomas E. Tavolara, Arijit Dutta, Martin V. Burks, Wei Chen, Wendy L. Frankel, Metin N. Gurcan, M. Khalid Khan Niazi:
Automatic generation of the ground truth for tumor budding using H&E stained slides. - David R. Chambers, Bradley B. Brimhall, Donald R. Poole Jr., Edward A. Medina:
Cancer cell segmentation for cellularity prediction via a weakly labeled/strongly labeled hybrid convolutional neural network. - Yoni Schirris, Mendel Engelaer, Andreas Panteli, Hugo Mark Horlings, Efstratios Gavves, Jonas Teuwen:
WeakSTIL: weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need. - Thomas E. Tavolara, M. Khalid Khan Niazi, Gary Tozbikian, Robert Wesolowski, Metin N. Gurcan:
Predicting HER2 scores from registered HER2 and H&E images. - Bi Song, Albert Huang, Ming-Chang Liu:
Deep ordinal regression for automatic tumor cellularity assessment from pathological images.
Multispectral, Multimodality, and Fused imaging
- Laura Quintana, Samuel Ortega, Raquel León, Himar Fabelo, Francisco Javier Balea-Fernández, Esther Sauras, Marylène Lejeune, Ramón Bosch, Carlos López, Gustavo M. Callicó:
In the use of artificial intelligence and hyperspectral imaging in digital pathology for breast cancer cell identification. - Ruining Deng, Haichun Yang, Zuhayr Asad, Zheyu Zhu, Shiru Wang, Lee E. Wheless, Agnes B. Fogo, Yuankai Huo:
Dense multi-object 3D glomerular reconstruction and quantification on 2D serial section whole slide images. - Ximing Zhou, Ling Ma, Hasan K. Mubarak, James V. Little, Amy Y. Chen, Larry L. Myers, Baran D. Sumer, Baowei Fei:
Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and deep learning. - Minh Ha Tran, Ling Ma, James V. Litter, Amy Y. Chen, Baowei Fei:
Thyroid carcinoma detection on whole histologic slides using hyperspectral imaging and deep learning.
Multi-Stain and multiplexed Image Analysis
- Wenchao Han, Alison M. Cheung, Dan Wang, Kela Liu, Martin J. Yaffe, Anne L. Martel:
Cell phenotyping using unsupervised clustering on multiplexed fluorescence images of breast cancer tissue specimens. - Shunxing Bao, Yucheng Tang, Ho Hin Lee, Riqiang Gao, Qi Yang, Xin Yu, Sophie Chiron, Lori A. Coburn, Keith T. Wilson, Joseph T. Roland, Bennett A. Landman, Yuankai Huo:
Inpainting missing tissue in multiplexed immunofluorescence imaging. - Alison M. Cheung, Dan Wang, Kela Liu, Sarah Hynes, Ben Wang, Simone Stone, Pamela Ohashi, Martin J. Yaffe:
Spatial analysis of cellular arrangement using quantitative, single-cell imaging of protein multiplexing. - Yiqing Shen:
Personalized stain style transfer layers for distributed histology classification. - Laurin Herbsthofer, Barbara Ehall, Martina Tomberger, Barbara Prietl, Thomas R. Pieber, Pablo López-García:
Procedural generation of synthetic multiplex immunohistochemistry images using cell-based image compression and conditional generative adversarial networks. - Farzad Fereidouni, Richard M. Levenson:
DUET dual-mode scanning of H&E slides reveals novel contrast.
Computational Pathology: from Research to Application
- Ling Ma, Armand Rathgeb, Minh Tran, Baowei Fei:
Unsupervised super resolution network for hyperspectral histologic imaging. - Darshana Govind, Saber Meamardoost, Rabi Yacoub, Rudiyanto Gunawan, John E. Tomaszewski, Pinaki Sarder:
Integrating image analysis with single cell RNA-seq data to study podocyte-specific changes in diabetic kidney disease. - Tianyuan Yao, Yuzhe Lu, Ruining Deng, Zheyu Zhu, Zuhayr Asad, Haichun Yang, Lee E. Wheless, Agnes B. Fogo, Yuankai Huo:
Self-supervised learning with large-scale web image mining for characterizing glomerular lesions.
Computer-Aided Diagnosis, prognosis, and predictive Analysis
- Samuel P. Border, Brandon Ginley, John E. Tomaszewski, Pinaki Sarder:
HistoLens: a generalizable tool for increasing accessibility and interpretability of quantitative analyses in digital pathology. - Salma Dammak, Matthew J. Cecchini, Aaron D. Ward:
Using deep learning to predict tumor mutational burden in lung squamous cell carcinoma from 20 centers. - Nicholas J. Lucarelli, Donghwan Yun, Dohyun Han, Brandon Ginley, Kyung Chul Moon, Avi Rosenberg, John Tomaszewski, Seung Seok Han, Pinaki Sarder:
Computational integration of renal histology and urinary proteomics using neural networks. - Aya Aqeel, Germán Corredor, Vidya Sankar Viswanathan, Chuheng Chen, Mogjan Mokhtari, Pingfu Fu, Joseph E. Willis, Anant Madabhushi:
Computer extracted features of tumor-infiltrating lymphocytes (TILs) architecture are prognostic of progression-free survival in stage III colon cancer. - Jon Camp, Gregory Otteson, Jansen Seheult, Min Shi, Dragan Jevremovic, Horatiu Olteanu, Ahmad Nanaa, Aref Al-Kali, Mohamed Salama, David R. Holmes III:
Deep neural network for cell type differentiation in myelodysplastic syndrome diagnosis performs similarly when trained on compensated or uncompensated data. - Chang Hee Han, Jin Tae Kwak:
A hybrid computational pathology method for the detection of perineural invasion junctions. - Seyed Hossein Mirjahanmardi, Melanie Dawe, Anthony Fyles, Wei Shi, Fei-Fei Liu, Susan Done, April Khademi:
Preserving dense features for Ki67 nuclei detection. - Luoting Zhuang, Jana Lipková, Richard Chen, Faisal Mahmood:
Deep learning-based integration of histology, radiology, and genomics for improved survival prediction in glioma patients.
Segmentation of Cellular and tissue Structures
- Saarthak Kapse, Rajarsi Gupta, Prateek Prasanna:
Shape-based tumor microenvironment analysis to differentiate non-small cell lung cancer subtypes: a radio-pathomic study. - Alvaro Sandino Garzon, Ruchika Verma, Yijang Chen, David Becerra Tovar, Eduardo Romero Castro, Pallavi Tiwari:
A hierarchical deep learning approach for segmentation of glioblastoma tumor niches on digital histopathology. - Jonathan Folmsbee, Scott Doyle, Rakesh Choudhary, Margaret Brandwein-Weber, Jawaria Rahman:
Cloud-based platform for human-in-the-loop re-annotation of whole slide imaging: large scale semantic pathology segmentation. - Huu-Giao Nguyen, Amjad Khan, Heather Dawson, Alessandro Lugli, Inti Zlobec:
Group affinity weakly supervised segmentation from prior selected tissue in colorectal histopathology images. - Junwei Deng, Yiqing Shen, Yi Guo, Jing Ke:
CellSegNet: an adaptive multi-resolution hybrid network for cell segmentation.
Poster Session
- Yabo Fu, Yang Lei, David M. Schuster, Sagar A. Patel, Jeffrey D. Bradley, Tian Liu, Xiaofeng Yang:
Deformable histopathology-MRI image registration using deep learning. - Varun Vasudev, Albert Xthona, Tom Kimpe:
Display systems for digital pathology: what are proper luminance, contrast and resolution settings? - David Morrison, David Harris-Birtill:
Anonymising pathology data using generative adversarial networks. - Sudhir Suman, Prateek Prasanna:
Muti-stage attention-based network for brain tumor subtype classification. - Yuxuan Shi, Quan Liu, Jiachen Xu, Zuhayr Asad, Can Cui, Hernán Correa, Yash Choksi, Girish Hiremath, Yuankai Huo:
Eosinophilic esophagitis multi-label feature recognition on whole slide imaging using transfer learning. - Yusuke Murayama, Tohru Sugiyama, Yoshihiko Ogino, Hiroki Furuta, Takaaki Kambara, Koi Kamisono, Tokiya Abe, Katsura Emoto, Akinori Hashiguchi, Yoichi Kajimura, Michiie Sakamoto:
Quantitative evaluation of color difference between actual specimens and whole-slide imaging-scanned images calibrated with commercial color charts. - Shayan Monabbati, Paula Toro, Kaustav Bera, Pingfu Fu, Sylvia A. Lou, Anant Madabhushi:
Quantitative histomorphometric features of tumor nuclei are prognostic of disease-free survival in papillary thyroid carcinoma. - Nikolai Fetisov, Lawrence O. Hall, Dmitry B. Goldgof, Matthew B. Schabath:
Survival time prediction from unannotated lung cancer histopathology images. - Antong Chen, Tosha Shah, Andrew Brown, Haleh Akrami, Albert Swiston, Amir Vajdi, Radha Krishnan, Razvan Cristescu:
Prediction of tumor mutation burden from H&E whole-slide images: a comparison of training strategies with convolutional neural networks. - Matthew McNeil, Cem Anil, Anne L. Martel:
Sparse capsule networks for informative representation learning in digital pathology. - Pierpaolo Vendittelli, Esther M. M. Smeets, Geert J. S. Litjens:
Automatic tumour segmentation in H&E-stained whole-slide images of the pancreas. - Neil Kavthekar, Brandon Ginley, Samuel P. Border, Nicholas J. Lucarelli, Kuang-Yu Jen, Pinaki Sarder:
Automated tubular morphometric visualization for whole kidney biopsy. - Zhengyao Peng, Chang Bian, Yang Du, Jie Tian:
A deep learning-based computational prediction model for characterizing cellular biomarker distribution in tumor microenvironment. - Mauro Gwerder, Amjad Khan, Christina Neppl, Inti Zlobec:
Detection of lung cancer metastases in lymph nodes using a multiple instance learning approach. - Brendon Lutnick, David Manthey, Jan U. Becker, Jonathan E. Zuckerman, Luis Rodrigues, Kuang Yu Jen, Pinaki Sarder:
A cloud-based tool for federated segmentation of whole slide images. - Digant Patel, Pranab Samanta, Ravi Kamble, Nitin Singhal:
RAE-Net: a deep learning system for staging of estrous cycle. - Michael Brehler, Allison K. Lowman, Samuel Bobholz, Savannah R. Duenweg, Fitzgerald Kyereme, Cassandra Naze, John Sherman, Kenneth A. Iczkowski, Peter S. LaViolette:
An automated approach for annotating Gleason patterns in whole-mount prostate cancer histology using deep learning. - Deepak Bajhaiya, Sujatha Narayanan Unni:
Deep learning-enabled classification of gastric ulcers from wireless capsule endoscopic images. - Parmida Ghahremani, Arie E. Kaufman:
CrowdDeep: deep-learning from the crowd for nuclei segmentation. - Ahmet Gokberk Gul, Özdemir Çetin, Christoph Reich, Nadine Flinner, Tim Prangemeier, Heinz Koeppl:
Histopathological image classification based on self-supervised vision transformer and weak labels. - Kouther Noureddine, Paul Gallagher, Griffin Thomas, Sylvia Lam, Gavin MacAulay, Anna McGuire, John English, Martial Guillaud, Calum MacAulay:
Investigating intra-tumor heterogeneity in non-small cell lung cancer using multiplexed immunohistochemistry and deep learning.
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