ScaleMAI: Accelerating the Development of Trusted Datasets and AI Models

• URL: http://arxiv.org/abs/2501.03410v1
• Date: Mon, 06 Jan 2025 22:12:00 GMT
• Title: ScaleMAI: Accelerating the Development of Trusted Datasets and AI Models
• Authors: Wenxuan Li, Pedro R. A. S. Bassi, Tianyu Lin, Yu-Cheng Chou, Xinze Zhou, Yucheng Tang, Fabian Isensee, Kang Wang, Qi Chen, Xiaowei Xu, Xiaoxi Chen, Lizhou Wu, Qilong Wu, Yannick Kirchhoff, Maximilian Rokuss, Saikat Roy, Yuxuan Zhao, Dexin Yu, Kai Ding, Constantin Ulrich, Klaus Maier-Hein, Yang Yang, Alan L. Yuille, Zongwei Zhou,
• Abstract summary: We propose ScaleMAI, an agent of AI-integrated data curation and annotation. First, ScaleMAI creates a dataset of 25,362 CT scans, including per-voxel annotations for benign/malignant tumors and 24 anatomical structures. Second, through progressive human-in-the-loop iterations, ScaleMAI provides Flagship AI Model that can approach the proficiency of expert annotators in detecting pancreatic tumors.
• Score: 46.80682547774335
• License:
• Abstract: Building trusted datasets is critical for transparent and responsible Medical AI (MAI) research, but creating even small, high-quality datasets can take years of effort from multidisciplinary teams. This process often delays AI benefits, as human-centric data creation and AI-centric model development are treated as separate, sequential steps. To overcome this, we propose ScaleMAI, an agent of AI-integrated data curation and annotation, allowing data quality and AI performance to improve in a self-reinforcing cycle and reducing development time from years to months. We adopt pancreatic tumor detection as an example. First, ScaleMAI progressively creates a dataset of 25,362 CT scans, including per-voxel annotations for benign/malignant tumors and 24 anatomical structures. Second, through progressive human-in-the-loop iterations, ScaleMAI provides Flagship AI Model that can approach the proficiency of expert annotators (30-year experience) in detecting pancreatic tumors. Flagship Model significantly outperforms models developed from smaller, fixed-quality datasets, with substantial gains in tumor detection (+14%), segmentation (+5%), and classification (72%) on three prestigious benchmarks. In summary, ScaleMAI transforms the speed, scale, and reliability of medical dataset creation, paving the way for a variety of impactful, data-driven applications.

Related papers

• A Data-Efficient Pan-Tumor Foundation Model for Oncology CT Interpretation [17.993838581176902]
  PASTA is a pan-tumor CT foundation model that achieves state-of-the-art performance on 45 of 46 representative oncology tasks. PASTA-Gen produces a comprehensive dataset of 30,000 CT scans with pixel-level annotated lesions and paired structured reports.
  arXiv  Detail & Related papers  (2025-02-10T05:45:03Z)
• Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification [2.5091334993691206]
  Development of a robust deep-learning model for retinal disease diagnosis requires a substantial dataset for training. The capacity to generalize effectively on smaller datasets remains a persistent challenge. We've combined a wide range of data sources to improve performance and generalization to new data.
  arXiv  Detail & Related papers  (2024-09-17T17:22:35Z)
• AutoPET Challenge: Tumour Synthesis for Data Augmentation [26.236831356731017]
  We adapt the DiffTumor method, originally designed for CT images, to generate synthetic PET-CT images with lesions. Our approach trains the generative model on the AutoPET dataset and uses it to expand the training data. Our findings show that the model trained on the augmented dataset achieves a higher Dice score, demonstrating the potential of our data augmentation approach.
  arXiv  Detail & Related papers  (2024-09-12T14:23:19Z)
• Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development [59.74920439478643]
  In this paper, we collect and annotated the first benchmark dataset that covers diverse ERUS scenarios. Our ERUS-10K dataset comprises 77 videos and 10,000 high-resolution annotated frames. We introduce a benchmark model for colorectal cancer segmentation, named the Adaptive Sparse-context TRansformer (ASTR)
  arXiv  Detail & Related papers  (2024-08-19T15:04:42Z)
• Texture Feature Analysis for Classification of Early-Stage Prostate Cancer in mpMRI [0.0]
  We analyze the contributions made by first-order statistical features, Haralick texture features, and local binary patterns to the classification. We identify a small set of features that determine the classification outcome, which may aid the development of explainable AI approaches.
  arXiv  Detail & Related papers  (2024-06-21T18:12:58Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO) [0.12175619840081271]
  We propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-Oncology MRI data. Our end-to-end framework i) classifies MRI sequences using an ensemble classifier, ii) preprocesses the data in a reproducible manner, and iv) delineates tumor tissue subtypes. It is robust to missing sequences and adopts an expert-in-the-loop approach, where the segmentation results may be manually refined by radiologists.
  arXiv  Detail & Related papers  (2022-10-06T18:23:42Z)
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
  We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents. We generate an automatic tumor boundary detector for the rare disease of glioblastoma. We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
  arXiv  Detail & Related papers  (2022-04-22T17:27:00Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)

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.