1:00pm - Registration

1:45pm - Welcome Addresses: Maryellen Giger (Chair of IWBI 2024) and Steven Montner (Interim Chair of Department of Radiology, University of Chicago)

2:30pm - KEYNOTE & FIRESIDE CHAT: State of Breast Cancer Screening, including realities and limitations worldwide - Olufunmilayo Olopade, MD, FACCR, OON, University of Chicago, and Benjamin O. Anderson, MD, FACS, University of Washington
Introduction: Maryellen Giger, University of Chicago, Chair
Moderator: Martin Yaffe, University of Toronto

4:00pm - Break

4:30pm - Walking Tour of University of Chicago Campus

6:00pm - Welcome Reception, with heavy hors d’oeuvres

8:30pm - Adjourn

7:30am - Registration and breakfast

8:30am - KEYNOTE: Multi-energy and Multi-modality Breast Cancer Imaging
- John A. Shepherd, PhD, University of Hawaii Cancer Center
Moderators: Maryellen Giger, University of Chicago, Chair and Karen Drukker, University of Chicago

9:30am - Break

Session 1 - Contrast-enhanced DBT/Mammography
Moderator: Martin Tornai, NIH/NIBIB

• 10:00am - Simulated image-specific microcalcification clusters and associated mass enhancement to enhance training of a deep learning model for cancer detection in contrast-enhanced mammography - Astrid Van Camp, Maastricht University

• 10:20am - Experimental results of the first prototype direct-indirect dual-layer flat-panel detector for contrast enhanced digital mammography and contrast enhanced digital breast tomosynthesis - Wei Zhao, Stony Brook University

• 10:40am - Quantitative imaging of Iodine-based contrast agent in dual-energy DBT - Emil Sidky, University of Chicago Medicine

Session 2 - DBT
Moderator: Ioannis Sechopoulos, Radboud University

• 11:00am - Fast wide-angle DBT with high in-plane resolution – system concept and first clinical results - Ludwig Ritschl, Siemens Healthineers AG

• 11:20am - A YOLO-based learning lesion classifier of pre-exposure scan in digital breast tomosynthesis - Seoyoung Lee, KAIST

• 11:40am - Sample-efficient framework for breast lesion detection in Digital Breast Tomosynthesis: preliminary analysis on its generalizability - Belayat Hossain, University of Pittsburgh

12:00pm - Lunch

1:00pm - Poster Sessions
Moderators: Heather Whitney, University of Chicago and Hui Li, University of Chicago

• Poster #1: Glandularity estimation in digital breast tomosynthesis with an accretion approach – Leonardo Coito Pereyra, Radboud University Medical Center

• Poster #2: Local dynamic reconstruction in digital breast tomosynthesis – Matteo Barbieri, GE healthcare

• Poster #3: Exploring Advanced 2D Acquisitions in Breast Tomosynthesis: T-shaped & Pentagon Geometries – Priyash Singh, University of Pennsylvania

• Poster #4: When simulation becomes real: Exploring the characteristics of a 3D-printed power-law phantom in tomosynthesis imaging – Ingrid Reiser, University of Chicago

• Poster #5: Evaluation of non-Gaussian statistical properties of digital breast tomosynthesis images – Kai Yang, Massachusetts General Hospital

• Poster #6: Added value of feature uncertainty in a radiomic analysis of contrast-enhanced digital mammography boosted by deep learning – Robert Marti, University of Girona

• Poster #7: Assessing the Feasibility of AI-Enhanced Portable Ultrasound for Improved Early Detection of Breast Cancer in Remote Areas – Nusrat Zaman Zemi – University of Hawaii Cancer Center

• Poster #8: Comparing contemporary breast imaging technologies for use in dense-breast supplemental screening – Martin Tornai, Duke University Medical Center

• Poster #9: Incorporating Longitudinal Screening Data into Image-Based Breast Cancer Risk Assessment – Tobias Wagner, KU Leuven

• Poster #10: Recovering unprocessed digital mammograms from processed mammograms for quantitative analysis – Olivier Alonzo, Sunnybrook Health Sciences Center

• Poster #11: Comparing percent breast density assessments of an AI-based method with expert reader estimates: inter-observer variability – Stepan Romanov, University of Manchester

• Poster #12: Differences in Longitudinal Changes of Mammographic Breast Percent Density among Normal, Benign, and Cancer Patients: A Preliminary Study – Robert Nishikawa, University of Pittsburgh

• Poster #13: The relationship between BMI, breast density, and cancer progression in breast cancer patients from Appalachian Kentucky – Braxton McFarland, University of Kentucky

• Poster #14: Explainability of An AI-Based Breast Cancer Risk Prediction Tool – Sam Ellis, Royal Surrey NHS Foundation Trust

• Poster #15: Accurate Estimation of Density and Background Parenchymal Enhancement in Breast MRI using Deep Regression and Transformers – Grey Kuling, Sunnybrook Research Institute

• Poster #16: Regional Disparities in Visual Assessment of Breast Density: Implications for Risk Stratification in Breast Cancer Detection – Serena Pacile, Therapixel

• Poster #17: Inter-manufacturer generalisation of AI on processed mammograms – Alistair Hickman, University of Surrey

• Poster #18: How to go with that flow? A perfusion phantom for the optimization of dynamic contrast-enhanced dedicated breast CT – Liselot Goris, University of Twente

• Poster #19: Survey of image processing used for mammography systems in the United Kingdom: how variable is it? – Alistair Mackenzie, Royal Surrey NHS Foundation Trust

• Poster #20: Spatial analysis of immune cells in breast cancer using k-nearest neighbor graphs and Louvain-community clustering of immunofluorescent protein multiplexing images – Alison Cheung, Sunnybrook Research Institute

• Poster #21: Sat2Nu: a modular deep learning pipeline for converting fat-suppressed breast MRIs to nonfat-suppressed images with foreseeable applications in abbreviated breast MRI – Nehal Doiphode, University of Pennsylvania

• Poster #22: Adaptive thresholding technique for segmenting breast dense tissue in digital breast – Tamerlan Mustafaev, University of Pittsburgh Medical Center

• Poster #23: Challenges with mammography of very thin breasts – John Loveland, Royal Surrey County Hospital

• Poster #24: Evaluation of subtraction processing for mammograms analyzed by breast density and thickness – Chiharu Kai, Niigata University of Health and Welfare

Session 3 - Breast Cancer Screening
Moderator: Susan Astley, University of Manchester

• 2:00pm - Further adventures in AI-directed double reading for single reading environments - Robert Nishikawa, University of Pittsburgh

• 2:20pm - Deep learning-based mammographic breast compression pressure estimates on processed images vs an unprocessed image reference - Melissa Hill, Volpara Health

• 2:40pm - Modelling the connection between image quality, cancer detection and overdiagnosis in breast imaging, a new perspective on DM and DBT - Magnus Dustler, Lund University

3:00pm - Break

Session 4 - Breast Density and Breast Cancer Risk
Moderator: Nico Karssemeijer, ScreenPoint Medical

• 3:30pm - Breast composition measurements from full-field digital mammograms using generative adversarial networks - Eloy Garcia, Universitat de Girona

• 3:50pm - Longitudinal analysis of micro-calcifications features for breast cancer risk prediction with the Mirai model - Yao-Kuan Wang, UZ Leuven

• 4:10pm - Advancing Volumetric Breast Density Segmentation: A Deep Learning Approach with Digital Breast Tomosynthesis - Nehal Doiphode, University of Pennsylvania

4:30 - 5:30pm - Current Controversies Around Imaging-Based Dense-Breast Cancer Assessment Panel Discussion - Christine Edmonds, MD, John Shepherd, PhD, Chisako Muramatsu, PhD, Ioannis Sechopolous, PhD, Kirti Kulkarni, MD, Leslie Ferris Yerger, MBA
Moderator: Martin Tornai, NIH/NIBIB

A tour of the Medical Imaging Labs will occur this evening. You will be able to sign up to participate in this tour onsite at the registration desk.

7:30am - Registration and breakfast

8:30am - KEYNOTE: The Future of Molecular Imaging/Theranostics in Breast Cancer
- Christine E. Edmonds, MD, Hospital of the University of Pennsylvania
Moderators: Anders Tingberg, Lund University and Yulei Jiang, University of Chicago

9:30am - Break

Session 5 - Devices and System Design
Moderator: Chisako Muramatsu, Shiga University

• 10:00am - 4D dynamic contrast-enhanced breast CT: Evaluation of quantitative accuracy - Juan Pautasso, Radboud University Medical Center

• 10:20am - Cascaded System Analysis of a Direct-indirect Dual-layer Flat-panel-detector for Contrast-enhanced Breast Imaging - Xiangyi Wu, Stony Brook Medicine

• 10:40am - Breast cancer diagnosis using extended-wavelength diffuse reflectance spectroscopy – comparing tumor subgroups - Nadia Chaudhry, Lund University

Session 6 - Image Processing
Moderator: Despina Kontos, Columbia University

• 11:00am - Asymmetric scatter kernel superposition-inspired deep learning approach to estimate scatter in digital breast tomosynthesis - Subong Hyun, KAIST

• 11:20am -Quantitative analysis of high-plex immunofluorescence microscopy images to investigate the breast cancer tumor microenvironment - Frederick Howard, University of Chicago

• 11:40am - Evaluating an image restoration pipeline for digital mammography across varied radiation exposures and microcalcification sizes using model observer analysis - Marcelo Andrade da Costa Vieira, University of São Paulo

12:00pm - Lunch

1:00pm - Poster Sessions
Moderators: Karen Drukker, University of Chicago and Ingrid Reiser, University of Chicago

• Poster #26: Customizable digital mammography database: on-demand generation with user-defined radiation dose and microcalcification cluster characteristics – Marcelo Andrade da Costa Vieira, University of São Paulo

• Poster #27: Creation of simulated mammography data to supplement machine learning training datasets – Anna Worthy, University of Surrey

• Poster #28: Simulation of heterogeneity within breast lesions based upon Perlin noise – Hanna Tomic, Lund University

• Poster #30: AI lesion risk score at different exposure settings – Anders Martin Tingberg, Skane University Hospital

• Poster #31: Alignment of clinical breast tomosynthesis and mechanical images: The effect of the variation in shift and rotation – Predrag Bakic, Lund University

• Poster #32: Characterization of invasive breast cancer lesions in breast x-ray imaging: a reference dataset for virtual imaging trials – Machteld Keupers, University Ziekenhuis Leuven

• Poster #33: Measuring effective X-ray attenuation coefficients of 3D printing materials for anthropomorphic breast phantoms – Adrian Belarra, Universidad Complutense de Madrid

• Poster #34: Noise measurements from a Quality Control programme tell a story – Kristina Tri Wigati, KU Leuven

• Poster #35: The IAEA activities to support quality and safety in X ray breast imaging – Olivera Ciraj Bjelac, International Atomic Energy Agency

• Poster #36: Evaluating the efficacy of automated breast arterial calcification quantification models in detecting BAC from mammograms with non-BAC calcifications – Kaier Wang, Volpara Health

• Poster #37: SAM-PR: Enhancing 3D automated breast ultrasound imaging segmentation with probabilistic refinement of SAM – Robert Marti, University of Girona

• Poster #38: Assessing the Impact of Counterfactuals for Textural Changes in Mammogram Classification – Ridhi Arora, University of Pittsburgh

• Poster #39: 3D Breast Ultrasound Image Classification Using 2.5D Deep learning – Zhikai Yang, KTH Royal Institute of Technology

• Poster #40: A Study on the Role of Radiomic Feature Stability in Predicting Breast Cancer Subtypes – Isabela Cama, Universita di Genova

• Poster #41: Mitigating Annotation Shift in Cancer Classification Using Single Image Generative Models – Oliver Díaz, University of Barcelona

• Poster #42: Exploring the possibility of extracting cancer morphology from deep feature cluster – Cory Thomas, Aberystwyth University

• Poster #43: Automatic Detection of Breast Cancer Lumpectomy Margin from Intraoperative Specimen Mammography – Braxton McFarland, University of Kentucky

• Poster #44: Slow-scan CE-DBT with focal spot steering – Priyash Singh, University of Pennsylvania

• Poster #45: Localization, segmentation, and classification of mammographic abnormalities using deep learning – Reyer Zwiggelaar, Aberystwyth University

• Poster #46: Segmentation and classification of mammographic abnormalities using local binary patterns and deep learning, Reyer Zwiggelaar, Aberystwyth University

• Poster #47: Learning general cancer distribution: generalization of an AI model to diagnostic images – Serena Pacile, Therapixel

• Poster #48: Time-to-event learning paradigm as a generalized approach to estimate risk of breast cancer using image based deep learning models – Serena Pacile, Therapixel

Session 7 - Virtual Clinical Trials
Moderator: Andrew Maidment, University of Pennsylvania

• 2:00pm - Applicability of virtual breast phantoms for detectability studies in synthetic mammography - Katrien Houbrechts, KU Leuven

• 2:20pm - Use of microsimulation modeling for research in breast cancer screening - Martin Yaffe, Sunnybrook Research Institute

• 2:40pm - Adding tissue variability to digital breast phantoms for mammography and digital breast tomosynthesis simulations - Gustavo Pacheco, Radboud University Medical Center

3:00pm - Break

Session 8 - Multi-modality Imaging, Optical Imaging, and Dosimetry
Moderator: Hilde Bosmans, KU Leuven

• 3:30pm - The added value of abbreviated MRI with UF DCE-MRI and DWI on digital breast tomosynthesis in diagnosing breast lesions - Akane Ohashi, Kyoto University Graduate School of Medicine

• 3:50pm - Developing ultrasound optical tomography for deep tissue imaging of the breast - Egle Bukarte, Lund University

• 4:10pm - Average glandular dose for contrast enhanced mammography examinations (CEM): a comparison between two centers - Najim Amallal EL Ouahabi, European University of Madrid

4:30 - 5:15pm - Role of Bias and Diversity in AI of Breast Cancer Imaging Panel Discussion -
Kristina Lång, MD, PhD, Karen Drukker, PhD, Ann-Katherine Carton, PhD, Andrew Maidment, PhD
Moderator: Heather Whitney, University of Chicago

The 17th IWBI Gala and Dinner will take place tonight, from 6:30pm-9:30pm. Admission is included with your registration.

7:30am - Registration and breakfast

8:30am - KEYNOTE: A breast radiologist’s perspective on AI - from experimental studies to randomized trials - Kristina Lång, MD, PhD, Lund University
Moderators: Elizabeth Krupinski, Emory University and Kirti Kulkarni, University of Chicago

9:30am - Break

Session 9 - Artificial Intelligence in Breast Imaging I
Moderator: Robert Marti, University of Girona

• 10:00am - Towards improved breast cancer detection on digital mammograms using local self-attention-based transformer - Han Chen/Anne Martel, Sunnybrook Research Institute

• 10:20am - Sureness of classification of breast cancers as pure DCIS or with invasive components on DCE-MRI - Heather Whitney, University of Chicago

• 10:40am - Longitudinal Interpretability of Deep Learning based Breast Cancer Risk Prediction model – Comparison of different Attribution Methods - Zan Klanecek, University of Ljubljana

Session 10 - Artificial Intelligence in Breast Imaging II
Moderator: Reyer Zwiggelaar, Aberystwyth University

• 11:00am - Explainable radiomics to characterize breast density and tissue complexity: preliminary findings- Vincent Dong, University of Pennsylvania

• 11:20am -Deep-Learning based Background Parenchymal Enhancement Quantification in Contrast Enhanced Mammography: an application to Neoadjuvant Chemotherapy - Ann-Katherine Carton, GE HealthCare

• 11:40am - Improving the CNNs Performance of Mammography Mass Classification via Binary Mask Knowledge Transfer - Reyer Zwiggelaar, Aberystwyth University

12:00pm - Closing Remarks: Maryellen Giger, University of Chicago, Chair and boxed lunch