mlsanityData to 7 Oct 2026

M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning

arXiv 2007.00453 ↗First posted 1 Jul 2020cs.CV cs.LGcited by 12

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Abstract (arXiv)

M3d-CAM is an easy to use library for generating attention maps of CNN-based PyTorch models improving the interpretability of model predictions for humans. The attention maps can be generated with multiple methods like Guided Backpropagation, Grad-CAM, Guided Grad-CAM and Grad-CAM++. These attention maps visualize the regions in the input data that influenced the model prediction the most at a certain layer. Furthermore, M3d-CAM supports 2D and 3D data for the task of classification as well as for segmentation. A key feature is also that in most cases only a single line of code is required for generating attention maps for a model making M3d-CAM basically plug and play.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2020.

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Citing papers per year: 2022: 3; 2023: 1; 2024: 7; 2025: 1.

12 papers.

  1. 2022Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Datacited by 17
  2. 2022Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learningcited by 5
  3. 2024Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretationcited by 3
  4. 2024One Wave To Explain Them All: A Unifying Perspective On Feature Attributioncited by 3
  5. 2024TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIscited by 3
  6. 2022Multiple Sclerosis Lesions Segmentation using Attention-Based CNNs in FLAIR Imagescited by 2
  7. 2024Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRIcited by 2
  8. 2025WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Carecited by 2
  9. 2023Atlas-Based Interpretable Age Prediction In Whole-Body MR Imagescited by 1
  10. 2024Multi-task Learning Approach for Intracranial Hemorrhage Prognosiscited by 0
  11. 2024Enhancing Trust in Clinically Significant Prostate Cancer Prediction with Multiple Magnetic Resonance Imaging Modalitiescited by 0
  12. 2024Adapting Visualization Techniques for Time-Series Anomaly Detection: From Convolutional Neural Networks to Convolutional-Recurrent Neural Networkscited by 0