ArticleComputational intelligence and neuroscience2022
Object Detection in Medical Images Based on Hierarchical Transformer and Mask Mechanism.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence for facial fracture detection: a systematic review.Oral radiology · 2026Pooled it
- Multi-plane vision transformer for hemorrhage classification using axial and sagittal MRI data.Scientific reports · 2026Article
- ITSRS: An Inverse Taylor Series Adaptive Loss Based on Synergized Regional-Structural Information for Medical Image Segmentation.Journal of imaging informatics in medicine · 2026Article
- Automated detection of fetal vascular malperfusion via data augmentation and algorithm improvement.Scientific reports · 2026Article
- Design of lightweight metal surface defect detection technology for YOLOv7-tiny using Anchor-Free algorithm.Scientific reports · 2026Article
- Optimizing RetinaNet anchors using differential evolution for improved object detection.Scientific reports · 2025Article
- Enhanced nuclear information fusion and visual transformer for pathological breast cancer image classification.Scientific reports · 2025Article
- From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on X-Ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- Intraoperative detection of parathyroid glands using artificial intelligence: optimizing medical image training with data augmentation methods.Surgical endoscopy · 2024Article
- Hybrid Ensemble Deep Learning Model for Advancing Ischemic Brain Stroke Detection and Classification in Clinical Application.Journal of imaging · 2024Article
- Multi-branch CNN and grouping cascade attention for medical image classification.Scientific reports · 2024Article
- A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound.BMC medical imaging · 2024Review
- Recognition of rare antinuclear antibody patterns based on a novel attention-based enhancement framework.Briefings in bioinformatics · 2024Article
- Stratum corneum nanotexture feature detection using deep learning and spatial analysis: a noninvasive tool for skin barrier assessment.GigaScience · 2024Article
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6 authors.
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Abstract
The object detection task in the medical field is challenging in terms of classification and regression. Due to its crucial applications in computer-aided diagnosis and computer-aided detection techniques, an increasing number of researchers are transferring the object detection techniques to the medical field. However, in existing work on object detection, researchers do not consider the low resolution of medical images, the high amount of noise, and the small size of the objects to be detected. Based on this, this paper proposes a new algorithmic model called the MS Transformer, where a self-supervised learning approach is used to perform a random mask on the input image to reconstruct the input features, learn a richer feature vector, and filter out excessive noise. To focus the model on the small objects that are being detected, the hierarchical transformer model is introduced in this paper, and a sliding window with a local self-attention mechanism is used to give a higher attention score to the small objects to be detected. Finally, a single-stage object detection framework is used to predict the sequence of sets at the location of the bounding box and the class of objects to be detected. On the DeepLesion and BCDD benchmark dataset, the model proposed in this paper achieves better performance improvement on multiple evaluation metric categories.
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