We will hold the workshop on Sunday 26th April: at 10:00 - 13:00 and 18:00 - 21:20 BST, as two webinars.

• Webinar 1 (10:00 - 13:00) comprises invited talks (pre-recorded, streamed on the ICLR website) and live Q&A on Zoom, followed by live panel discussion on Zoom.

• Webinar 2 (18:00 - 21:20) will consist of contributed talks (pre-recorded, streamed on the ICLR website).

We will use Rocket.Chat for discussion throughout the day - please ask your questions on our workshop channel !

Webinar 1: Invited Talks (10:00 - 13:00 BST)

Speaker

10:10 Invited talk 1 Chris Paton (University of Oxford)

Clinician’s view of the potential of AI for healthcare

10:50 Invited talk 2 Edward Choi (KAIST Graduate School of AI)

Knowledge graphs and representation learning for Electronic Health Records

11:30 Invited talk 3 Sotirios A Tsaftaris (University of Edinburgh)

Disentangled representation learning in medical imaging

Webinar 2: Contributed talks (18:00 - 21:20 BST)

White papers

Oral

#5 An Overview and Case Study of the Clinical AI Model Development Life Cycle for Healthcare Systems

Charles J. Lu, Julia A. Strout, Romane Gauriau, Brad C. Wright, Fabiola Bezerra De Carvalho Marcruz, Varun Buch, Katherine P. Andriole

Poster

#6 Towards Fully-Automated Carotid Plaque Risk Prediction Using 3-D Freehand Ultrasound

Branislav Holländer, Stefania Paperini, Mina Sedra, Jacob Eckert

Research papers

Oral

#7 IROF: a low resource evaluation metric for explanation methods

Laura Rieger, Lars Kai Hansen

#8 Optimizing Medical Treatment for Sepsis in Intensive Care: from Learning to Pre-Trial

Luchen Li, Ignacio Albert-Smet, Aldo A. Faisal

#9 Predicting Unplanned Readmissions with Highly Unstructured Data

Constanza Fierro, Jorge Pérez, Javier Mora

#10 Assessing Robustness to Noise: Low-Cost Head CT Triage

Sarah Hooper, Jared Dunnmon, Matthew Lungren, Sanjiv Sam Gambhir, Christopher Ré, Adam Wang, Bhavik Patel

#11 Towards a robust and affordable approach for automated malaria diagnosis from microscopy images

Liang Liang, Bill Sun

#12 Towards a predictive spatio-temporal representation of brain data

Tiago Azevedo, Luca Passamonti, Pietro Liò, Nicola Toschi

#13 XGMix: Local-Ancestry Inference with Stacked XGBoost

Arvind Kumar, Daniel Mas Montserrat, Carlos Bustamante, Alexander Ioannidis

Poster

#14 Teacher-Student Domain Adaptation for Biosensor Models

Lawrence Phillips, David B. Grimes, Yihan (Jessie) Li

#15 Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention

Tianyi Zhang, Yun Gu, Xiaolin Huang, Enmei Tu, Jie Yang

#16 Image Quality Transfer Enhances the Contrast and Resolution of Low-Field MRI in African Paediatric Epilepsy Patients

Matteo Figini, Hongxiang Lin, Godwin Ogbole, Felice D'Arco, David W Carmichael, Stefano B Blumberg, Ryutaro Tanno, Enrico Kaden, Biobele J Brown, Ikeoluwa Lagunju, Helen J Cross, Delmiro Fernandez-Reyes, Daniel C Alexander

#17 Teacher-student chain for efficient semi-supervised histology image classification

Shayne Shaw, Maciej Pajak, Aneta Lisowska, Sotirios A Tsaftaris , Alison Q O'Neil

#18 Understanding the robustness of deep neural network classifiers for breast cancer screening

Witold Oleszkiewicz, Stanisław Jastrzębski, Taro Makino, Tomasz Trzciński, Linda Moy, Kyunghyun Cho, Laura Heacock, Krzysztof J. Geras

#19 Uncertainty Estimation in Cancer Survival Prediction

Hrushikesh Loya, Pranav Poduval, Deepak Anand, Neeraj Kumar, Amit Sethi

#20 Heterogeneity loss to handle intersubject and intrasubject variability

Shubham Goswami, Suril Mehta, Dhruva Sahrawat, Anubha Gupta, Ritu Gupta

#21 Breast Cancer Detection Using Convolutional Neural Networks

Simon Hadush Nrea, Abiot Sinamo Boltena, Yaecob Girmay Gezahegn, Gebrekirstos Hagos, Getnet Asfaw, Abel Kahsay

#22 Train, Learn, Expand, Repeat.

Abhijeet Parida, Aadhithya Sankar, Rami Eisawy, Tom Finck, Benedikt Wiestler, Franz Pfister, Julia Moosbauer

#23 Improve robustness of DNN for ECG signal classification: a noise-to-signal ratio perspective

Linhai Ma, Liang Liang