Dual-Channel Reliable Breast Ultrasound Image Classification Based on Explainable Attribution and Uncertainty Quantification
This paper focuses on the classification task of breast ultrasound images and researches on the reliability measurement of classification results. We proposed a dual-channel evaluation framework based on the proposed inference reliability and predictive reliability scores. For the inference reliability evaluation, human-aligned and doctor-agreed inference rationales based on the improved feature attribution algorithm SP-RISA are gracefully applied. Uncertainty quantification is used to evaluate the predictive reliability via the Test Time Enhancement. The effectiveness of this reliability evaluation framework has been verified on our breast ultrasound clinical dataset YBUS, and its robustness is verified on the public dataset BUSI. The expected calibration errors on both datasets are significantly lower than traditional evaluation methods, which proves the effectiveness of our proposed reliability measurement.
Code (0)
등록된 구현이 없습니다.
Tasks
image-classificationImage ClassificationUncertainty QuantificationSimilar Papers 제목 키워드 기반
AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images
Various deep learning methods have been proposed to segment breast lesion from ultrasound images. However, similar intensity distributions, variable tumor morphology and blurred boundaries present challenges for breast l…
Lesion SegmentationSegmentationGlobal Guidance Network for Breast Lesion Segmentation in Ultrasound Images
Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. Segmenting breast regions accurately from ultrasound image is a challe…
Boundary DetectionImage SegmentationLesion SegmentationMedical Image Segmentation+2MDA-Net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images
Accurate breast lesion segmentation is a great help in the initial stage of breast cancer treatment planning. Ultrasound is considered the safe and cheapest method for the breast screening process. However, ultrasound im…
Lesion SegmentationSegmentationClassification of Breast Cancer Lesions in Ultrasound Images by using Attention Layer and loss Ensembles in Deep Convolutional Neural Networks
Reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low cost method for early diagnosis of breast cancer. The accuracy of the diagnosis is however h…
ClassificationGeneral ClassificationTransfer LearningESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation
Breast cancer is one of the common cancers that endanger the health of women globally. Accurate target lesion segmentation is essential for early clinical intervention and postoperative follow-up. Recently, many convolut…
Lesion SegmentationSegmentationTumor Segmentation