AMIA's Annual Symposium is the premier learning and networking conference attended by more than 2,500 health informaticians from across the world. Now, you can access full presentations and slides from the live event at your convenience while earning CME/CNE online.
AMIA 2024 Annual Symposium On Demand is designed to provide you with the very latest health informatics content with maximum value and convenience. Revisit one or all top 20 sessions from the conference, featuring leading voices from across the informatics field. Choose the format that fits your preferred learning style. Take up to two years to claim your education credits. Recorded at AMIA’s Annual Symposium, held November 9-13, 2024, in San Francisco, CA.
Choose Your Format
Presenter, Slides, and Audio
Members: $150 $120*
Nonmembers: $210 $168*
*Includes 20% discount through March 31. Log in to see your discount.
Slides and Audio
Members: $120 $96*
Nonmembers: $170 $136*
*Includes 20% discount through March 31. Log in to see your discount.
Robust Visual Identification of Under-resourced Dermatological Diagnoses with Classifier-Steered Background Masking
Collecting images of rare dermatological diseases for machine learning detection applications is a costly, laborious task. It is difficult to collect enough images of these diagnoses to avoid the risk of low accuracy "in the wild." One of the sources of bias in these networks is irrelevant background pixel data. These pixels necessarily have no clinical significance, yet Deep Neural Networks will make weak correlations based on that information. To reduce their ability to do this, we introduce a masking augmentation algorithm, InfoMax-Cutout. It employs unsupervised Information Maximization losses to mask out background pixels. InfoMax-Cutout increased accuracy on classifying 319 diagnoses by 0.76%. These features generalized to an unseen diagnosis task (Fitzpatrick 17k), improving accuracy over a baseline by 43.3% and reducing Gini inequality by 20.9%. This approach of learning to separate out background pixels can increase accuracy in detecting diseases in Lower and Middle Income Countries.
Learning Outcomes
- Describe how imaging ML models can fail to generalize due to irrelevant background information, and how to address these failures.
Speakers
- Miguel Dominguez, PhD, VisualDx
A Multi-Task Learning Approach for Segmentation of Breast Arterial Calcifications in Screening Mammograms
Screening mammogram is a standard imaging procedure to measure breast cancer risk among 45+ year old women. Quantifying breast arterial calcification (BAC) from screening mammograms is a non-invasive and cost-efficient approach to assess the future risk of adverse cardiovascular events among women, such as heart attack and stroke. However, segmentation of breast arterial calcification is an involved task and poses several technical challenges such as extremely small BAC finding, low breast arteries to breast area ratio in the mammogram images, tissue features such as breast folds and heterogeneous density, have very similar imaging appearance. In this work, we aim to address the shortcomings of existing SOTA methods, e.g., SCUNet, and analyze the comparative performance. Given the fact that we will not be able to simply resize mammogram to preserve the resolution, we adopted a patch-based methodology for segmentation using the original resolution which may hinder the model understanding of whole mammogram. We propose a multi-task learning approach for patch-based BAC segmentation by adding an auxiliary task of patch position prediction which force the model to learn breast anatomy to comprehend the locations where BAC will not occur, such as breast boundary. The proposed method achieves state-of-the-art performance compared to the baselines. To demonstrate the utility, we also validate our method on external data and provide survival analysis for CVD based on the BAC score and provide a comparison with CAC score.
Learning Outcomes
- Understand the methodology for patch-based segmentation for smaller image findings.
- Describe the role of novel BAC biomarkers for MACE risk assessment
Speakers
- Imon Banerjee, PhD, Arizona State U, Mayo Clinic
Project Elucidate: Web-Based Single Cell Annotation Tool For Building Deep Segmentation Models on Stimulated Raman Histology
Single-cell analysis of cancer histology offers crucial insights into the tumor microenvironment. In brain cancer research, an advanced imaging technique called Stimulated Raman Histology (SRH) allows for rapid digital imaging of brain tumor biopsies without requiring tissue staining. SRH microscopes combined with AI are currently being used for intraoperative tumor classification in neurosurgery. However, there is yet to be single-cell annotations for SRH. Project Elucidate proposes a collaborative, open-source web platform for building cell segmentation AI models in SRH.
Learning Outcomes
- Evaluate advancements in AI-based cell segmentation.
- Discuss Stimulated Raman Histology (SRH).
- Annotate cells on SRH using Elucidate.
- Train a deep segmentation model using cell annotations
Speakers
Abhishek Bhattacharya, M.D., NYU Langone
Variogram Modeling of Spatially Variant Early Response to Concurrent Chemo- and Immunotherapy for Metastatic Non-Small Cell Lung Cancer
Predicting response of metastatic non-small cell lung cancer (mNSCLC) to chemo-immunotherapy (chemoICI) by incorporating the spatial correlation structure of PET imaging has potential to support clinical decisions regarding patient- and lesion-level risk stratification. As a prelude to extending our previous framework, the “Voxel Forecast” multiscale regression for predicting spatially variant tumor response, we explored different variograms models of spatial correlation in the mNSCLC chemoICI response stetting.
Learning Outcomes
- State the significance of variogram models in analyzing the spatial correlation structure of FDG PET/CT imaging for metastatic non-small cell lung cancer (mNSCLC).
- Apply variogram models to predict treatment response in patients with mNSCLC based on FDG PET/CT imaging data.
Speakers
- Faisal Yaseen, PhD student
Continuing Education Credit
Physicians
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American Medical Informatics Association designates this online enduring material for 1.0 AMA PRA Category 1™ credits. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.
Nurses
The American Medical Informatics Association is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center’s Commission on Accreditation.
- Approved Contact Hours: 1.0 participant maximum
- Nurse planner for this activity: Jenna Thate, PhD, RN, CNE
- Jenna Thate discloses that she has no financial relationships with ACCME/ANCC-defined ineligible companies.
Upon completion of each video and corresponding evaluation portion of this activity, all learners will be able to download the appropriate credit certificate, or a certificate of participation.
Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.
ACHIPsTM
AMIA Health Informatics Certified ProfessionalsTM (ACHIPsTM) can earn 1 professional development unit (PDU) per contact hour.
ACHIPsTM may use CME/CNE certificates or the ACHIPsTM Recertification Log to report 2024 Symposium sessions attended for ACHIPsTM Recertification.
Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.