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🛠️ LEOPARD CHALLENGE WORKSHOP AT MICCAI 2024

We are happy to invite you to the LEOPARD challenge workshop - a satellite event at MICCAI 2024. The workshop will be held at:

🕓 6-th October 2024, 16:00 - 18:00 (4 pm - 6 pm)

📌 Room Jade, Palmeraie Palace building, Palmeraie Rotana Resort, Marrakesh , Morocco

🖼️ The posters will be located at the Conference Center building, Room Atlas.

The session will include both posters and oral presentations. Check below the program details.

PROGRAM

  • 16:00-16:20: AI for Prostate Pathology (Prof. Geert Litjens)

  • 16:20-16:30: Challenge Setup (Khrystyna Faryna)

  • 16:30-16:40: Predicting Biochemical Recurrence in Prostate Cancer using Foundation Models (MEVIS-ProSurvival - Till Nicke, Raphael Schäfer, Lars Ole Schwen and Johannes Lotz)

  • 16:40-16:50: Multiple Instance Learning for Recurrence Prediction of Prostate Cancer with Whole Slide Images (HITSZLab - Songhan Jiang, Zijie Fang, Xiangming Yan, Qi Ouyang, and Yongbing Zhang)

  • 16:50-17:00: Time to BCR prediction using TransMIL network parameterized with modified MSE Loss (AIRAMatrix - Surya Achanta, Raja Fida, Owaish, Aditya Vartak, Nilanjan Chattopadhyay, and Nitin Singhal)

  • 17:00-17:10: Predicting biochemical recurrence in prostate cancer with multiple instance learning (Paicon - Witali Aswolinskiy and Christian Aichmüller)

  • 17:10-17:20: Creating Interpretable Recurrence Predictions for Prostate Cancer Through Automatic Gleason Grading (MartelLab - Matthew McNeil and Anne Martel)

  • 17:20-17:30: Results Announcement and Awards Ceremony (Khrystyna Faryna)

  • 17:30-18:00: Poster Session

ACCEPTED POSTERS

  • PathHG:A Pathology Hypergraph Computation Method for Survival Prediction (iMoonLab - Xiangmin Han, Huijian Zhou, Biaoliang Guan, Jihua Zhu, Zhiqiang Tian, and Yue Gao)

  • Learning Biochemical Recurrence of Prostate Cancer From Histopathology Slides Using Transformer based Correlated Multiple Instance Learning (KatherLab - Li Zhang, Aaron Kutzer, Narmin Ghaffari Laleh, Jakob Nikolas Kather)

  • Solution for Estimating Biochemical Recurrence on Prostate Whole Slide Images (QuIIL - Doanh C. Bui, Anh T. Nguyen, Sunhong Park and Jin Tae Kwak)

  • Biochemical Prostate Cancer Recurrence Prediction: Thinking Fast & Slow (IUComPath - Suhang You, Sanyukta Adap, Siddhesh Thakur, Bhakti Baheti, and Spyridon Bakas)

  • SoftMIL (SoftwareMill - Kamil Rzechowski)

  • Transformer-based multiple instance learning to predict the biochemical recurrence of prostate cancer (SmileLab - Xulin Chen and Junzhou Huang)

  • MAC-MIL: Multi-head Attention-Challenging Multiple Instance Learning for Survival Analysis (BioMediA -Muhammad Ridzuan, Salma Hassan, Dawlat Akaila, Numan Saeed, and Mohammad Yaqub)

  • Survival Analysis via Ranked Probability Loss with Non-Uniform Label Smoothing for Biochemical Prostate Cancer Recurrence from Histopathological Data (agaldran - Adrian Galdran)

The results and materials will be made publicly available after the workshop.