mlsanityData to 7 Oct 2026

Boosting Medical Image Classification with Segmentation Foundation Model

arXiv 2406.11026 ↗First posted 16 Jun 2024cs.CV cs.AIcited by 8

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Abstract (arXiv)

The Segment Anything Model (SAM) exhibits impressive capabilities in zero-shot segmentation for natural images. Recently, SAM has gained a great deal of attention for its applications in medical image segmentation. However, to our best knowledge, no studies have shown how to harness the power of SAM for medical image classification. To fill this gap and make SAM a true ``foundation model'' for medical image analysis, it is highly desirable to customize SAM specifically for medical image classification. In this paper, we introduce SAMAug-C, an innovative augmentation method based on SAM for augmenting classification datasets by generating variants of the original images. The augmented datasets can be used to train a deep learning classification model, thereby boosting the classification performance. Furthermore, we propose a novel framework that simultaneously processes raw and SAMAug-C augmented image input, capitalizing on the complementary information that is offered by both. Experiments on three public datasets validate the effectiveness of our new approach.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 16 Jun 2024.

Builds on

16 of 23 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2023Segment Anythingcited by 8,158
  4. 2016Densely Connected Convolutional Networkscited by 6,691
  5. 2017Squeeze-and-Excitation Networkscited by 4,763
  6. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  7. 2017Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)cited by 549
  8. 2023Customized Segment Anything Model for Medical Image Segmentationcited by 277
  9. 2019Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosiscited by 165
  10. 2023Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imagingcited by 161
  11. 2023Polyp-SAM: Transfer SAM for Polyp Segmentationcited by 64
  12. 2023Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Modelcited by 56
4 more
  1. 2019Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Imagescited by 39
  2. 2017Data-Driven Color Augmentation Techniques for Deep Skin Image Analysiscited by 13
  3. 2017Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesionscited by 8
  4. 2022Data-Driven Deep Supervision for Skin Lesion Classificationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 1.

8 papers.

  1. 2024SAUGE: Taming SAM for Uncertainty-Aligned Multi-Granularity Edge Detectioncited by 7
  2. 2025Topo-VM-UNetV2: Encoding Topology into Vision Mamba UNet for Polyp Segmentationcited by 5
  3. 2025TopoImages: Incorporating Local Topology Encoding into Deep Learning Models for Medical Image Classificationcited by 2
  4. 2025Integrating Multi-scale and Multi-filtration Topological Features for Medical Image Classificationcited by 2
  5. 2025Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Modelscited by 1
  6. 2025White Light Specular Reflection Data Augmentation for Deep Learning Polyp Detectioncited by 1
  7. 2025Adapting a Segmentation Foundation Model for Medical Image Classificationcited by 0
  8. 2026Prompt-Free SAM-Based Multi-Task Framework for Breast Ultrasound Lesion Segmentation and Classificationcited by 0