The RISE 2023 Journal Club aims to create a friendly environment to discuss the latest state-of-the-art papers in the areas of medical image analysis, AI and computer vision. The main objective of this bi-weekly reading group is to help you develop/improve your critical and design thinking skills, which are essential skills for researchers and will help you when presenting or writing your own work.
To date, we have held two editions in which participants joined us remotely from different continents in highly engaging and stimulating sessions.
During each session, the moderators briefly introduce the paper and then moderate a discussion where everyone is welcome to provide their thoughts and ask any questions on the paper. The topics of the papers will vary, and we will try to cover different areas of medical data analysis, e.g., registration, segmentation, federated learning, fairness, and reinforcement learning —among others. Similarly, we will review papers from the machine and deep learning communities, providing you with a broader overview of the state-of-the-art method.
For more about RISE-MICCAI and the RISE 2023 Journal Club, check our website at http://www.miccai.org/about-miccai/rise-miccai/
Follow us on GitHub at https://github.com/RISE-MICCAI
And join our mailing list to receive updates about our RISE activities at https://bit.ly/2VMPHXc
| No | Date | Moderator | Title | Link |
|---|---|---|---|---|
| 26 | 04/05/2024 | Pamela Guevara | Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data | https://www.sciencedirect.com/science/article/pii/S1053811922006656 |
| 25 | 04/05/2024 | Tareen Dawood | Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis | https://arxiv.org/pdf/2303.03242 |
| 24 | 20/04/2024 | Islem Rekik | Special session: The journey of a research paper: from writing to review | |
| 23 | 06/04/2024 | Zhen Yuan | Orthogonal annotation benefits barely-supervised medical image segmentation | https://arxiv.org/abs/2303.13090 |
| 22 | 23/03/2024 | Charles Delahunt | Consistent Individualized Feature Attribution for Tree Ensembles | https://arxiv.org/abs/1802.03888 |
| 21 | 09/03/2024 | Charles Delahunt | Understanding metric-related pitfalls in image analysis validation | https://arxiv.org/abs/2302.01790 |
| 20 | 24/02/2024 | Qiang Zhang | Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy and Artificial intelligence for contrast-free MRI: Scar assessment in myocardial infarction using deep learning–based virtual native enhancement | https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.122.060137 and https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.121.054432 |
| 19 | 13/01/2024 | Charles, Andrea, Ahmed, Tareen and Esther | Opening session multi-linguistic: french, spanish, english, arabic | |
| 18 | 13/12/2023 | Tiarna Lee | An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation | https://arxiv.org/abs/2308.13415 |
| 17 | 29/11/2023 | Prosper Oyibo | Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings | https://doi.org/10.1117/1.JMI.10.4.044005 |
| 16 | 15/11/2023 | Tareen Dawood | Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images | https://www.sciencedirect.com/science/article/pii/S1361841523001214 |
| 15 | 01/11/2023 | Prerak Mody and Mortiz Fuchs | Prediction Variability to Identify Reduced AI Performance in Cancer Diagnosis at MRI and CT | https://doi.org/10.1148/radiol.230275 |
| 14 | 20/09/2023 | Ayantika Das | Diffusion Autoencoders: Toward a Meaningful and Decodable Representation | https://openaccess.thecvf.com/content/CVPR2022/papers/Preechakul_Diffusion_Autoencoders_Toward_a_Meaningful_and_Decodable_Representation_CVPR_2022_paper.pdf |
| 13 | 06/09/2023 | Charles | Metrics to guide development of machine learning algorithms for malaria diagnosis | https://arxiv.org/abs/2209.06947 |
| 12 | 26/07/2023 | Miguel López-Pérez | Disentangling human error from the ground truth in segmentation of medical images | https://arxiv.org/abs/2007.15963 |
| 11 | 12/07/2023 | Yasar Mehmood | Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training | https://arxiv.org/abs/2303.00874 |
| 10 | 28/06/2023 | Samra Irshad | STEEX: Steering Counterfactual Explanations with semantics | https://arxiv.org/abs/2111.09094 |
| 9 | 14/06/2023 | Islem Rekik | HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients | https://arxiv.org/pdf/2010.01264 |
| 8 | 03/06/2023 | Charles | Attention Is All You Need | https://arxiv.org/abs/1706.03762 |
| 7 | 20/05/2023 | Islem Rekik | On Predicting Generalization using GANs | https://openreview.net/pdf?id=eW5R4Cek6y6 |
| 6 | 06/05/2023 | Charles and Andrea | Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization | https://arxiv.org/abs/1404.1100 |
| 5 | 22/04/2023 | Charles Delahunt | A tutorial on Principal Component Analysis | https://arxiv.org/abs/1404.1100 |
| 4 | 08/04/2023 | Charles and Islem | A dirty dozen: 12 p-value misconceptions | http://mcb112.org/w06/Goodman08.pdf |
| 3 | 25/03/2023 | Islem and Charles | Coherent Gradients: An Approach to Understanding Generalization in Gradient Descent-based Optimization | https://openreview.net/pdf?id=ryeFY0EFwS |
| 2 | 11/03/2023 | Charles and Andrea | Imagenet classification with deep convolutional neural networks | https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf |
| 1 | 25/02/2023 | Islem Rekik | Hierarchical Reconstruction of 7T-like Images from 3T MRI Using Multi-level CCA and Group Sparsity | https://link.springer.com/chapter/10.1007/978-3-319-24571-3_79 |