CanvOI, an Oncology Intelligence Foundation Model: Scaling FLOPS Differently

• URL: http://arxiv.org/abs/2409.02885v1
• Date: Wed, 4 Sep 2024 17:15:44 GMT
• Title: CanvOI, an Oncology Intelligence Foundation Model: Scaling FLOPS Differently
• Authors: Jonathan Zalach, Inbal Gazy, Assaf Avinoam, Ron Sinai, Eran Shmuel, Inbar Gilboa, Christine Swisher, Naim Matasci, Reva Basho, David B. Agus,
• Abstract summary: We present CanvOI, a ViT-g/10-based foundation model designed to enhance the capabilities of digital pathology. By introducing larger tile sizes (380 x 380 pixels) and smaller patch sizes (10 x 10 pixels), we were able to optimize the model's performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The rapidly evolving field of digital oncopathology faces significant challenges, including the need to address diverse and complex clinical questions, often involving rare conditions, with limited availability of labeled data. These limitations hinder the development of robust AI-driven tools in the biomedical space, where accuracy in probabilistic determinations is of utmost importance. To address this, digital pathology foundation models have begun to emerge, typically developed with the size and diversity of the pre-training dataset and model parameters in mind. Here, we present CanvOI, a ViT-g/10-based foundation model designed to enhance the capabilities of digital pathology by addressing these challenges through a different approach. Considering the unique nature of oncologic histopathological images and the requirements from the embeddings to provide meaningful representations for Multiple Instance Learning (MIL) downstream models, we chose to modify the input image characteristics. By introducing larger tile sizes (380 x 380 pixels) and smaller patch sizes (10 x 10 pixels), we were able to optimize the model's performance, pushing computational resources in a new direction and achieving state-of-the-art performance on cancer-related benchmarks. CanvOI demonstrated a 1.5-7.4% improvement in averaged AUC compared to other leading foundation models built for digital pathology. Moreover, our results demonstrate that CanvOI significantly outperformed the other models, with the performance gap widening substantially when trained on just 10% of the initial cohort. This work highlights an alternative approach that, if integrated with traditional development approaches, has the potential to advance Oncology Intelligence (OI), overcome some of the current barriers and ultimately improve the clinical outcome of cancer patients.

Related papers

• ONCOPILOT: A Promptable CT Foundation Model For Solid Tumor Evaluation [3.956274064760269]
  ONCOPILOT is an interactive radiological foundation model trained on approximately 7,500 CT scans covering the whole body. It performs 3D tumor segmentation using visual prompts like point-click and bounding boxes, outperforming state-of-the-art models. ONCOPILOT also accelerates measurement processes and reduces inter-reader variability.
  arXiv  Detail & Related papers  (2024-10-10T13:36:49Z)
• Rethinking Model Prototyping through the MedMNIST+ Dataset Collection [0.11999555634662634]
  This work presents a benchmark for the MedMNIST+ database to diversify the evaluation landscape. We conduct a thorough analysis of common convolutional neural networks (CNNs) and Transformer-based architectures, for medical image classification. Our findings suggest that computationally efficient training schemes and modern foundation models hold promise in bridging the gap between expensive end-to-end training and more resource-refined approaches.
  arXiv  Detail & Related papers  (2024-04-24T10:19:25Z)
• Structured Model Pruning for Efficient Inference in Computational Pathology [2.9687381456164004]
  We develop a methodology for pruning the widely used U-Net-style architectures in biomedical imaging. We empirically demonstrate that pruning can compress models by at least 70% with a negligible drop in performance.
  arXiv  Detail & Related papers  (2024-04-12T22:05:01Z)
• Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation [113.5002649181103]
  Training open-source small multimodal models (SMMs) to bridge competency gaps for unmet clinical needs in radiology. For training, we assemble a large dataset of over 697 thousand radiology image-text pairs. For evaluation, we propose CheXprompt, a GPT-4-based metric for factuality evaluation, and demonstrate its parity with expert evaluation. The inference of LlaVA-Rad is fast and can be performed on a single V100 GPU in private settings, offering a promising state-of-the-art tool for real-world clinical applications.
  arXiv  Detail & Related papers  (2024-03-12T18:12:02Z)
• Leveraging Foundation Models for Content-Based Medical Image Retrieval in Radiology [0.14631663747888957]
  Content-based image retrieval has the potential to significantly improve diagnostic aid and medical research in radiology. Current CBIR systems face limitations due to their specialization to certain pathologies, limiting their utility. We propose using vision foundation models as powerful and versatile off-the-shelf feature extractors for content-based medical image retrieval.
  arXiv  Detail & Related papers  (2024-03-11T10:06:45Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Generative Adversarial Networks for Stain Normalisation in Histopathology [2.2166690647926037]
  One of the significant roadblocks to current research is the high level of visual variability across digital pathology images. Sten normalisation aims to standardise the visual profile of digital pathology images without changing the structural content of the images. This is an ongoing field of study as researchers aim to identify a method which efficiently normalises pathology images to make AI models more robust and generalisable.
  arXiv  Detail & Related papers  (2023-08-05T11:38:05Z)
• Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
  Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
  arXiv  Detail & Related papers  (2022-04-09T13:04:36Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• 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)
• Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks [62.9447303059342]
  We show the importance of this problem in medical community. We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence. We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
  arXiv  Detail & Related papers  (2020-07-15T09:22:55Z)

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.