INSIGHT: Explainable Weakly-Supervised Medical Image Analysis

• URL: http://arxiv.org/abs/2412.02012v2
• Date: Sun, 08 Dec 2024 16:58:40 GMT
• Title: INSIGHT: Explainable Weakly-Supervised Medical Image Analysis
• Authors: Wenbo Zhang, Junyu Chen, Christopher Kanan,
• Abstract summary: INSIGHT is a novel weakly-supervised aggregator that integrates heatmap generation as an inductive bias. On CT and WSI benchmarks, INSIGHT achieves state-of-the-art classification results and high weakly-labeled semantic segmentation performance.
• Score: 20.635521620900978
• License:
• Abstract: Due to their large sizes, volumetric scans and whole-slide pathology images (WSIs) are often processed by extracting embeddings from local regions and then an aggregator makes predictions from this set. However, current methods require post-hoc visualization techniques (e.g., Grad-CAM) and often fail to localize small yet clinically crucial details. To address these limitations, we introduce INSIGHT, a novel weakly-supervised aggregator that integrates heatmap generation as an inductive bias. Starting from pre-trained feature maps, INSIGHT employs a detection module with small convolutional kernels to capture fine details and a context module with a broader receptive field to suppress local false positives. The resulting internal heatmap highlights diagnostically relevant regions. On CT and WSI benchmarks, INSIGHT achieves state-of-the-art classification results and high weakly-labeled semantic segmentation performance. Project website and code are available at: https://zhangdylan83.github.io/ewsmia/

Related papers

• Deep Homography Estimation for Visual Place Recognition [49.235432979736395]
  We propose a transformer-based deep homography estimation (DHE) network. It takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification. Experiments on benchmark datasets show that our method can outperform several state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-25T13:22:17Z)
• UM-CAM: Uncertainty-weighted Multi-resolution Class Activation Maps for Weakly-supervised Fetal Brain Segmentation [15.333308330432176]
  We propose a novel weakly-supervised method with image-level labels based on semantic features and context information exploration. Our proposed method outperforms state-of-the-art weakly-supervised methods with image-level labels.
  arXiv  Detail & Related papers  (2023-06-20T12:21:13Z)
• Exploring Weakly Supervised Semantic Segmentation Ensembles for Medical Imaging Systems [11.693197342734152]
  We propose a framework for reliable classification and detection of medical conditions in images. Our framework achieves that by first utilizing lower threshold CAMs to cover the target object with high certainty. We have demonstrated an improved dice score of up to 8% on BRATS and 6% on DECATHLON datasets.
  arXiv  Detail & Related papers  (2023-03-14T13:31:05Z)
• GAN-based Medical Image Small Region Forgery Detection via a Two-Stage Cascade Framework [12.24879640482427]
  A new attack called CT-GAN has emerged, which can inject or remove lung cancer lesions to CT scans. Because the tampering region may even account for less than 1% of the original image, even state-of-the-art methods are challenging to detect the traces of such tampering. This paper proposes a cascade framework to detect GAN-based medical image small region forgery like CT-GAN.
  arXiv  Detail & Related papers  (2022-05-30T15:21:09Z)
• Boosting Few-shot Semantic Segmentation with Transformers [81.43459055197435]
  TRansformer-based Few-shot Semantic segmentation method (TRFS) Our model consists of two modules: Global Enhancement Module (GEM) and Local Enhancement Module (LEM)
  arXiv  Detail & Related papers  (2021-08-04T20:09:21Z)
• Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation [76.2621758731288]
  We tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA) We show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.
  arXiv  Detail & Related papers  (2021-08-03T17:09:56Z)
• CAMERAS: Enhanced Resolution And Sanity preserving Class Activation Mapping for image saliency [61.40511574314069]
  Backpropagation image saliency aims at explaining model predictions by estimating model-centric importance of individual pixels in the input. We propose CAMERAS, a technique to compute high-fidelity backpropagation saliency maps without requiring any external priors.
  arXiv  Detail & Related papers  (2021-06-20T08:20:56Z)
• PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization [64.39761523935613]
  We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding. It also exploits correlations between the different semantic levels of CNN to better localize anomalies.
  arXiv  Detail & Related papers  (2020-11-17T17:29:18Z)
• High resolution weakly supervised localization architectures for medical images [3.7117844677482146]
  We propose a model for high-accuracy weakly-supervised localization that achieved 0.62 average point localization accuracy on NIH's Chest X-Ray 14 dataset. Our experiments suggest that Global Average Pooling (GAP) and Group Normalization are the main culprits that worsen the localization accuracy of CAM.
  arXiv  Detail & Related papers  (2020-10-22T06:42:00Z)
• Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps [78.2581910688094]
  This work introduces a novel self-enhancement method to harvest accurate object localization maps and object boundaries with only category labels as supervision. In particular, the proposed Self-Enhancement Maps achieve the state-of-the-art localization accuracy of 54.88% on ILSVRC.
  arXiv  Detail & Related papers  (2020-06-09T12:35:55Z)
• CAFENet: Class-Agnostic Few-Shot Edge Detection Network [19.01453512012934]
  We tackle a novel few-shot learning challenge, which we call few-shot semantic edge detection. We also present a Class-Agnostic Few-shot Edge detection Network (CAFENet) based on meta-learning strategy.
  arXiv  Detail & Related papers  (2020-03-18T14:18:59Z)

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.