mlsanityData to 14 Oct 2026

Weakly supervised deep learning-based intracranial hemorrhage localization

arXiv 2105.00781 ↗First posted 3 May 2021cs.CV physics.med-phcited by 3

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Abstract (arXiv)

Intracranial hemorrhage is a life-threatening disease, which requires fast medical intervention. Owing to the duration of data annotation, head CT images are usually available only with slice-level labeling. This paper presents a weakly supervised method of precise hemorrhage localization in axial slices using only position-free labels, which is based on multiple instance learning. An algorithm is introduced that generates hemorrhage likelihood maps and finds the coordinates of bleeding. The Dice coefficient of 58.08 % is achieved on data from a publicly available dataset.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 6 of its 24 references that are in the map. First posted on arXiv 3 May 2021.

Builds on

6 of 24 references.

References · 6

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  4. 2018Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scanscited by 24
  5. 2020A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scanscited by 5
  6. 2020Localization and classification of intracranialhemorrhages in CT datacited by 1

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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2023Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learningcited by 6
  2. 2024Weakly Supervised Intracranial Hemorrhage Segmentation with YOLO and an Uncertainty Rectified Segment Anything Modelcited by 3
  3. 2023Class Activation Map-based Weakly supervised Hemorrhage Segmentation using Resnet-LSTM in Non-Contrast Computed Tomography imagescited by 1