Application of Improved YOLOv5 in the Detection and Automatic Correction of Noise Labels in Pathological Images
Pathological image cell detection is a fundamental part of medical diagnosis,and accurate detection of targeted cells and their quantities is crucial for disease diagnosis and treatment.Traditional medicine uses manual microscopy to estimate pathological images,relying on the work experience of pathologists,which leads to subjectivity and low detection accuracy.To this end,an improved YOLOv5 noise label detection and automatic correction network is proposed to detect target cells in pathological images.By using Conf and IOU functions,the net-work has the ability to distinguish between truth labels and noise labels,thereby achieving automatic correction of noise labels to assist doctors in clinical diagnosis of sinusitis disease types.The results showed that the improved network achieved an average accuracy and recall rate of 88.9％and 95.6％respectively on the pathological image dataset of sinusitis,which can meet the requirements of detecting pathological images and correcting noise labels.
digital pathological imageunsupervisionnoise labelsdeep learningself-correction
万方数据
维普
