MONet: Multi-scale Overlap Network for Duplication Detection in Biomedical Images

• URL: http://arxiv.org/abs/2207.09107v1
• Date: Tue, 19 Jul 2022 07:25:43 GMT
• Title: MONet: Multi-scale Overlap Network for Duplication Detection in Biomedical Images
• Authors: Ekraam Sabir, Soumyaroop Nandi, Wael AbdAlmageed, Prem Natarajan
• Abstract summary: We propose a multi-scale overlap detection model to detect duplicated image regions. It achieves state-of-the-art performance overall and on multiple biomedical image categories.
• Score: 20.533739598331646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Manipulation of biomedical images to misrepresent experimental results has plagued the biomedical community for a while. Recent interest in the problem led to the curation of a dataset and associated tasks to promote the development of biomedical forensic methods. Of these, the largest manipulation detection task focuses on the detection of duplicated regions between images. Traditional computer-vision based forensic models trained on natural images are not designed to overcome the challenges presented by biomedical images. We propose a multi-scale overlap detection model to detect duplicated image regions. Our model is structured to find duplication hierarchically, so as to reduce the number of patch operations. It achieves state-of-the-art performance overall and on multiple biomedical image categories.

Related papers

• QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge [93.61262892578067]
  Uncertainty in medical image segmentation tasks, especially inter-rater variability, presents a significant challenge. This variability directly impacts the development and evaluation of automated segmentation algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ)
  arXiv  Detail & Related papers  (2024-03-19T17:57:24Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys [99.7082441544384]
  We present BiomedJourney, a novel method for counterfactual biomedical image generation by instruction-learning. We use GPT-4 to process the corresponding imaging reports and generate a natural language description of disease progression. The resulting triples are then used to train a latent diffusion model for counterfactual biomedical image generation.
  arXiv  Detail & Related papers  (2023-10-16T18:59:31Z)
• Multi-Prompt Fine-Tuning of Foundation Models for Enhanced Medical Image Segmentation [10.946806607643689]
  The Segment Anything Model (SAM) is a powerful foundation model that introduced revolutionary advancements in natural image segmentation. In this study, we introduce a novel fine-tuning framework that leverages SAM's ability to bundle and process multiple prompts per image.
  arXiv  Detail & Related papers  (2023-10-03T19:05:00Z)
• A Novel Vision Transformer with Residual in Self-attention for Biomedical Image Classification [8.92307560991779]
  This article presents the novel framework of multi-head self-attention for vision transformer (ViT) The proposed method uses the concept of residual connection for accumulating the best attention output in each block of multi-head attention. The results show the significant improvement over traditional ViT and other convolution based state-of-the-art classification models.
  arXiv  Detail & Related papers  (2023-06-02T15:06:14Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference [28.409679398886304]
  We develop a novel model named Mixed-UNet, which has two parallel branches in the decoding phase. We evaluate the designed Mixed-UNet against several prevalent deep learning-based segmentation approaches on our dataset collected from the local hospital and public datasets.
  arXiv  Detail & Related papers  (2022-05-06T08:37:02Z)
• BioFors: A Large Biomedical Image Forensics Dataset [22.32517325828983]
  We present BioFors -- the first dataset for benchmarking common biomedical image manipulations. BioFors comprises 47,805 images extracted from 1,031 open-source research papers. We benchmark BioFors on all tasks with suitable state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2021-08-30T02:39:13Z)
• Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
  The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. In this paper, we develop a novel generative method named generative adversarial U-Net. Our newly designed model is domain-free and generalizable to various medical images.
  arXiv  Detail & Related papers  (2021-01-12T23:02:26Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Microscopic fine-grained instance classification through deep attention [7.50282814989294]
  Fine-grained classification of microscopic image data with limited samples is an open problem in computer vision and biomedical imaging. We propose a simple yet effective deep network that performs two tasks simultaneously in an end-to-end manner. The result is a robust but lightweight end-to-end trainable deep network that yields state-of-the-art results.
  arXiv  Detail & Related papers  (2020-10-06T15:29:58Z)

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.