mlsanityData to 7 Oct 2026

Delving into Ipsilateral Mammogram Assessment under Multi-View Network

arXiv 2309.02197 ↗First posted 5 Sep 2023cs.CVcited by 8

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Abstract (arXiv)

In many recent years, multi-view mammogram analysis has been focused widely on AI-based cancer assessment. In this work, we aim to explore diverse fusion strategies (average and concatenate) and examine the model's learning behavior with varying individuals and fusion pathways, involving Coarse Layer and Fine Layer. The Ipsilateral Multi-View Network, comprising five fusion types (Pre, Early, Middle, Last, and Post Fusion) in ResNet-18, is employed. Notably, the Middle Fusion emerges as the most balanced and effective approach, enhancing deep-learning models' generalization performance by +2.06% (concatenate) and +5.29% (average) in VinDr-Mammo dataset and +2.03% (concatenate) and +3% (average) in CMMD dataset on macro F1-Score. The paper emphasizes the crucial role of layer assignment in multi-view network extraction with various strategies.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 5 Sep 2023.

Builds on

11 of 21 references.

References · 11

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2016An overview of gradient descent optimization algorithmscited by 1,021
  4. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  5. 2019Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screeningcited by 94
  6. 2022VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammographycited by 83
  7. 2017High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networkscited by 25
  8. 2021Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detectioncited by 22
  9. 2021A novel multi-view deep learning approach for BI-RADS and density assessment of mammogramscited by 15
  10. 2022Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisationcited by 12
  11. 2023In-context Cross-Density Adaptation on Noisy Mammogram Abnormalities Detectioncited by 4

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Cited by year

Citing papers per year: 2024: 5; 2025: 3.

8 papers.

  1. 2025Enhancing Vietnamese VQA through Curriculum Learning on Raw and Augmented Text Representationscited by 4
  2. 2024Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognitioncited by 3
  3. 2024Blurry-Consistency Segmentation Framework with Selective Stacking on Differential Interference Contrast 3D Breast Cancer Spheroidcited by 2
  4. 2025A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusioncited by 2
  5. 2024Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cellscited by 1
  6. 2024MamT4: Multi-view Attention Networks for Mammography Cancer Classificationcited by 1
  7. 2025Modality-Specific Enhancement and Complementary Fusion for Semi-Supervised Multi-Modal Brain Tumor Segmentationcited by 1
  8. 2024Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosiscited by 0