About

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Adam Casson

I'm a research engineer at Paige.AI where I work on neural networks for detecting cancer in large-scale histopathology imagery.

Experience

Paige.AI, Senior Research Engineer 2019-Present
  • Working on weakly supervised and self supervised neural networks applied to histopathology images (100,000px by 100,000px in size).
  • Developed network architecture that improved detection and localization of cancer.
  • Core contributor to the research and development of Paige Prostate which received the first ever FDA approval of an AI system in pathology (read the paper here).
  • Worked on developing ML training framework to increase reproducibility, experiment tracking, code modularity, and lowering the barrier of entry to running experiments.
  • Involved in hiring most of the AI team by helping shape the interview process and doing 100+ technical interviews.
  • Helped lead research and development of a weakly supervised breast cancer detection system (paper coming soon).
Comcast-NBCUniversal, Machine Learning Engineer 2017-2019
  • Lead research of temporal language modeling for understanding semantic drift.
  • Worked on facial recognition, object detection, and scene detection for long-form videos.
  • Organized and taught a weekly machine learning course for coworkers.
Rochester Institute of Technology, Research Assistant 2016-2017
  • Researched visual question answering (VQA) on video data.
  • Developed a multi-modal network to jointly reason over videos and natural language to answer questions about a given video.
  • Created a new video VQA dataset by using existing video captioning dataset by converted captions to question-answer pairs.
  • Developed a hand-crafted system using dependency parsing to generate diverse sets of question-answer pairs from captions.

Education

Rochester Institute of Technology, B.S. Imaging Science 2013-2017

Publications

A Genomics-Driven Artificial Intelligence-Based Model Classifies Breast Invasive Lobular Carcinoma and Discovers CDH1 Inactivating Mechanisms (opens in a new tab)

Cancer Research 2024

Pareja, F., Dopeso, H., Wang, Y.K., Gazzo, A.M., Brown, D.N., Banerjee, M., Selenica, P., Bernhard, J.H., Derakhshan, F., da Silva, E.M., Colon-Cartagena, L., Basili, T., Marra, A., Sue, J., Ye, Q., Da Cruz Paula, A., Yildirim, S.Y., Pei, X., Safonov, A., Green, H., Gill, K.Y., Zhu, Y., Lee, M.C.H., Godrich, R.A., Casson, A., Weigelt, B., Riaz, N., Wen, H.Y., Brogi, E., Mandelker, D.L., Hanna, M.G., Kunz, J.D., Rothrock, B., Chandarlapaty, S., Kanan, C., Oakley, J., Klimstra, D.S., Fuchs, T.J., Reis-Filho, J.S.



Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology (opens in a new tab)

arXiv preprint (arXiv:2408.00738) 2024

Zimmermann, E., Vorontsov, E., Viret, J., Casson, A., Zelechowski, M., Shaikovski, G., Tenenholtz, N., Hall, J., Fuchs, T., Fusi, N., Liu, S., Severson, K.



A foundation model for clinical-grade computational pathology and rare cancers detection (opens in a new tab)

Nature Medicine 2024

Vorontsov, E.†, Bozkurt, A.†, Casson, A.†, Shaikovski, G.†, Zelechowski, M.†, Severson, K.†, Zimmermann, E., Hall, J., Tenenholtz, N., Fusi, N., Yang, E., Mathieu, P., van Eck, A., Lee, D., Viret, J., Robert, E., Wang, Y.K., Kunz, J.D., Lee, M.C.H., Bernhard, J.H., Godrich, R.A., Oakley, G., Millar, E., Hanna, M., Wen, H., Retamero, J.A., Moye, W.A., Yousfi, R., Kanan, C., Klimstra, D.S., Rothrock, B., Liu, S., Fuchs, T.J.



PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology (opens in a new tab)

arXiv preprint (arXiv:2405.10254) 2024

Shaikovski, G.†, Casson, A.†, Severson, K., Zimmermann, E., Wang, Y., Kunz, J.D., Retamero, J.A., Oakley, G., Klimstra, D., Kanan, C., Hanna, M., Zelechowski, M., Viret, J., Tenenholtz, N., Hall, J., Fusi, N., Yousfi, R., Hamilton, P., Moye, W.A., Voronstov, E., Liu, S., Fuchs, T.J.



Adadpting Self-Supervised Learning for Computational Pathology (opens in a new tab)

CVPR Workshop on Data Curation and Augmentation in Medical Imaging (DCAMI) 2024

Zimmermann, E., Tenenholtz, N., Hall, J.B., Shaikovski, G., Zelechowski, M., Casson, A., Milletari, F., Viret, J., Voronstov, E., Liu, S., Severson, K.A



Joint Breast Neoplasm Detection and Subtyping using Multi-Resolution Network Trained on Large-Scale H&E Whole Slide Images with Weak Labels (opens in a new tab)

Medical Imaging with Deep Learning (MIDL) 2023

(Oral presentation, MedIA special issue selectee)

Casson, A.†, Liu, S.†, Godrich, R.A., Aghdam, H., Lee, D., Rothrock, B., Kanan, C., Retamero, J., Hanna, M., Millar, E., Klimstra, D., Fuchs, T.



Clinical validation of artificial intelligence-augmented pathology diagnosis demonstrates significant gains in diagnostic accuracy in prostate cancer detection (opens in a new tab)

Archives of Pathology and Laboratory Medicine 2022

Raciti, P., Sue, J., Retamero, J.A., Ceballos, R., Godrich, R., Kunz, J.D., Casson, A., Thiagarajan, D., Ebrahimzadeh, Z., Viret, J., Lee, D., Schüffler, P.J., DeMuth, G., Gulturk, E., Kanan, C., Rothrock, B., Reis-Filho, J., Klimstra, D.S., Reuter, V., Fuchs, T.J.



Independent real-world application of a clinical-grade automated prostate cancer detection system (opens in a new tab)

The Journal of Pathology 2021

Silva, L.M., Pereira, E.M., Salles, P.G., Godrich, R., Ceballos, R., Kunz, J.D., Casson, A., Viret, J., Chandarlapaty, S., Ferreira, C.G., Ferrari, B., Rothrock, B., Raciti, P., Reuter, V., Dogdas, B., DeMuth, G., Sue, J., Kanan, C., Grady, L., Fuchs, T.J., Reis-Filho, J.S.

Peer-reviewed abstracts

Novel Mechanisms of CDH1 Inactivation in Breast Invasive Lobular Carcinoma Unveiled by the Integration of Artificial Intelligence and Genomics (opens in a new tab)

San Antonio Breast Cancer Symposium (SABCS) 2023

Pareja, F., Dopeso, H., Wang, Y., Gazzo, A., Brown, D., Selenica, P., Bernhard, J., Derakhshan, F., da Silva, E.M., Colon-Cartagena, L., Basili, T., Marra, A., Sue, J., Ye, Q., Da Cruz Paula, A., Yeni, S., Pei, X., Green, H., Gill, K., Zhu, Y., Lee, M., Godrich, R., Casson, A., Weigelt, B., Riaz, N., Wen, H.Y., Brogi, E., Hanna, M., Mandelker, D., Kunz, J., Rothrock, B., Chandarlapaty, S., Kanan, C., Oakley III, G., Klimstra, D., Fuchs, T., Reis-Filho, J.



Alternative molecular mechanisms underpinning breast invasive lobular carcinoma identified by genomics-driven artificial intelligence model (opens in a new tab)

European Society for Medical Oncology (ESMO) 2023

Fresia, P., Dopeso, H., Wang, Y., Gazzo, A.M., Brown, D.N., Selenica, P., Bernhard, J.H., Sue, J., Lee, M.C.H., Godrich, R.A., Casson, A., Weigelt, B., Hanna, M.G., Kunz, J.D., Rothrock, B., Kanan, C., Oakley, J., Klimstra, D.S., Fuchs, T.J., Reis-Filho, J.S.



Detection of invasive lobular carcinoma using an artificial intelligence algorithm based on genetic ground truth (opens in a new tab)

United States & Canadian Academy of Pathology Annual Meeting (USCAP) 2023

Fresia, P., Dopeso, H., Wang, Y., Goldfinger, M., Gazzo, A., Derakhshan, F., da Silva, E.M., Selenica, P., Basili, T., Danielle, S., Brown, D., Sue, J., Qiqi, Y., Da Cruz Paula, A., Monami, B., Lee, M., Godrich, R., Casson, A., Weigelt, B., Wen, H., Brogi, E., Hanna, M., Kunz, J., Kanan, C., Klimstra, D., Fuchs, T., Reis-Filho, J.



An artificial intelligence-based predictor of CDH1 biallelic mutations and invasive lobular carcinoma (opens in a new tab)

San Antonio Breast Cancer Symposium (SABCS) 2021

Reis-Filho, J.S., Pareja, F., Derakhshan F., Brown D.N., Sue, J., Selenica, P., Wang, Y.K., Da Cruz Paula, A., Banerjee, M., Ebrahimzadeh, Z., Isava, M., Lee, M., Godrich, R., Casson, A., Padron, R., Shaikovski, G., van Eck, A., Marra, A., Dopeso, H., Wen H.Y., Brogi, E., Hanna, M.G., Kanan, C., Kunz, J.D., Geyer, F.C., Leibowitz, C., Klimstra, D., Grady, L., Fuchs, T.J.



Morphological Breast Cancer Subtyping by Weakly Supervised Neural Networks (opens in a new tab)

United States & Canadian Academy of Pathology Annual Meeting (USCAP) 2021

Hanna, M., Lee, M., Bozkurt, A., Godrich, R., Casson, A., Raciti, P., Sue, J., Viret, J., Lee, D., Grady, L., Rothrock, B., Dogdas, B., Fuchs, T., Reis-Filho, J., Kanan, C.



Clinical-grade detection of breast cancer in biopsies and excisions using machine learning (opens in a new tab)

San Antonio Breast Cancer Symposium (SABCS) 2020

Hanna, M., Raciti, P., Godrich, R., Casson, A., Viret, J., Lee, D., Lee, M., Bozkurt, A., Sue, J., Dogdas, B., Rothrock, B., Grady, L., Kanan, C., Fuchs, T.



Digital MammaPrint and BluePrint using machine learning and whole slide imaging (opens in a new tab)

San Antonio Breast Cancer Symposium (SABCS) 2020

Glas, A.M., Reis-Filho, J.S., Wehkamp, D., Dogdas, B., Delahaye, L., Godrich, R., Mollink, J., Casson, A., Witteveen, A., Viret, J., Lee, D., Lee, M., Horlings, H., Grady, L., Fuchs, T., Audeh, W., Kanan, C., van't Veer, L.J.



Computational pathological identification of prostate cancer following neoadjuvant treatment (opens in a new tab)

American Society of Clinical Oncology Annual Meeting (ASCO) 2020

Dogdas, B., Kanan, C., Raciti, P., Tian, S.K., Brookman-May, S.D., Wetherhold, L., Smith, A., Rooney, O.B., McCarthy, S.A., Alvarez, J.D., Lopez-Gitlitz, A., Casson, A., Godrich, R., Kunz, J.D., Ceballos, R, Leibowitz, C., Grady, L., Fuchs, T.J.



† Equal contribution.

© Adam Casson.