The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data

• URL: http://arxiv.org/abs/2306.10484v2
• Date: Sun, 25 Jun 2023 09:56:03 GMT
• Title: The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
• Authors: Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle, Robin Schon, Katja Ludwig, Rainer Lienhart, Simon Jegou, Guang Li, Cong Chen, Qi Wang, Derik Shi, Mayug Maniparambil, Dominik Muller, Silvan Mertes, Niklas Schroter, Fabio Hellmann, Miriam Elia, Ine Dirks, Matias Nicolas Bossa, Abel Diaz Berenguer, Tanmoy Mukherjee, Jef Vandemeulebroucke, Hichem Sahli, Nikos Deligiannis, Panagiotis Gonidakis, Ngoc Dung Huynh, Imran Razzak, Reda Bouadjenek, Mario Verdicchio, Pasquale Borrelli, Marco Aiello, James A. Meakin, Alexander Lemm, Christoph Russ, Razvan Ionasec, Nikos Paragios, Bram van Ginneken, and Marie-Pierre Revel Dubois
• Abstract summary: This study implements the Type Three (T3) challenge format, which allows for training solutions on private data. With T3, challenge organizers train a provided by the participants on sequestered training data. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815.
• Score: 60.94672667514737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.HSUXJM-TNZF9CHSUXJM-TNZF9C

Related papers

• Optimistic Verifiable Training by Controlling Hardware Nondeterminism [22.85808027490485]
  We propose a method that combines training in a higher precision than the target model, rounding after intermediate steps, and storing rounding decisions. We achieve exact training replication at FP32 precision for both full-training and fine-tuning of ResNet-50 (23M) and GPT-2 (117M) models.
  arXiv  Detail & Related papers  (2024-03-14T17:44:35Z)
• SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data [51.42380508231581]
  The paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023) The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.
  arXiv  Detail & Related papers  (2023-11-09T13:02:04Z)
• The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge [19.72232714668029]
  The INSTANCE 2022 was a grand challenge held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing. The winner method achieved an average DSC of 0.6925, demonstrating a significant growth over our proposed baseline method.
  arXiv  Detail & Related papers  (2023-01-09T11:48:05Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark [48.30502612686276]
  Lung cancer is one of the deadliest cancers, and its effective diagnosis and treatment depend on the accurate delineation of the tumor. Human-centered segmentation, which is currently the most common approach, is subject to inter-observer variability. The 2018 VIP Cup started with a global engagement from 42 countries to access the competition data. In a nutshell, all the algorithms proposed during the competition, are based on deep learning models combined with a false positive reduction technique.
  arXiv  Detail & Related papers  (2022-01-03T03:06:38Z)
• The Second Place Solution for ICCV2021 VIPriors Instance Segmentation Challenge [6.087398773657721]
  The Visual Inductive Priors(VIPriors) for Data-Efficient Computer Vision challenges ask competitors to train models from scratch in a data-deficient setting. We introduce the technical details of our submission to the ICCV 2021 VIPriors instance segmentation challenge. Our approach can achieve 40.2%AP@0.50:0.95 on the test set of ICCV 2021 VIPriors instance segmentation challenge.
  arXiv  Detail & Related papers  (2021-12-02T09:23:02Z)
• Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence [79.038671794961]
  We launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK.
  arXiv  Detail & Related papers  (2021-11-18T00:43:41Z)
• Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge [1.52292571922932]
  We present the strategy we applied to participate in the MIDOG 2021 competition. The purpose of the competition was to evaluate the generalization of solutions to images acquired with unseen target scanners. We propose a solution based on a combination of state-of-the-art deep learning methods.
  arXiv  Detail & Related papers  (2021-09-02T10:09:02Z)
• Two-Stream Consensus Network: Submission to HACS Challenge 2021 Weakly-Supervised Learning Track [78.64815984927425]
  The goal of weakly-supervised temporal action localization is to temporally locate and classify action of interest in untrimmed videos. We adopt the two-stream consensus network (TSCN) as the main framework in this challenge. Our solution ranked 2rd in this challenge, and we hope our method can serve as a baseline for future academic research.
  arXiv  Detail & Related papers  (2021-06-21T03:36:36Z)
• 2nd Place Solution to ECCV 2020 VIPriors Object Detection Challenge [24.368684444351068]
  We show that by using state-of-the-art data augmentation strategies, model designs, and post-processing ensemble methods, it is possible to overcome the difficulty of data shortage and obtain competitive results. Our overall detection system achieves 36.6$%$ AP on the COCO 2017 validation set using only 10K training images without any pre-training or transfer learning weights ranking us 2nd place in the challenge.
  arXiv  Detail & Related papers  (2020-07-17T09:21:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.