The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP’s clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP.

Background: The successful recognition of benign and malignant breast nodules using ultrasound images is based mainly on supervised learning that requires a large number of labeled images. However, because high-quality labeling is expensive and time-consuming, we hypothesized that semi-supervised learning could provide a low-cost and powerful alternative approach. This study aimed to develop an accurate semi-supervised recognition method and compared its performance with supervised methods and sonographers. Methods: The faster region-based convolutional neural network was used for nodule detection from ultrasound images. A semi-supervised classifier based on the mean teacher model was proposed to recognize benign and malignant nodule images. The general performance of the proposed method on two datasets (8,966 nodules) was reported. Results: The detection accuracy was 0.88±0.03 and 0.86±0.02, respectively, on two testing sets (1,350 and 2,220 nodules). When 800 labeled training nodules were available, the proposed semi-supervised model plus 4,396 unlabeled nodules performed better than the supervised learning model (area under the curve (AUC): 0.934±0.026 vs. 0.83±0.050; 0.916±0.022 vs. 0.815±0.049). The performance of the semi-supervised model trained on 800 labeled and 4,396 unlabeled nodules was close to that of the supervised learning model trained on a massive number of labeled nodules (n=5,196) (AUC: 0.934±0.026 vs. 0.952±0.027; 0.916±0.022 vs. 0.918±0.017). Moreover, the semi-supervised model was better than the average accuracy of five human sonographers (AUC: 0.922 vs. 0.889). Conclusions: The semi-supervised model can achieve excellent performance for nodule recognition and be useful for medical sciences. The method reduced the number of labeled images required for training, thus significantly alleviating the difficulty in data preparation of medical artificial intelligence.

PURPOSE: To develop a novel scatter correction method without additional patient dose for dual-energy digital mammography (DEDM) to reduce scatter's impacts and enhance microcalcification detectability in dual-energy X-ray subtraction image. METHODS: Combining scatter radiation is lower spatial frequency component and calcifications are sparsely distributed in digital mammogram, we develop a new scatter correction strategy. First, an adaptive sampling scheme is presented to find possible noncalcification (zero calcification) pixels. Then the maximum likelihood expectation maximization (MLEM) algorithm is applied to evaluate initial scatter surface. The accurate scatter radiation of sampling pixels is obtained by solving dual-energy computational formula with zero calcification constraint and scatter surface constraint. RESULTS: After scatter correction, the scatter-to-primary ratio (SPR) of wedge phantom is reduced from ∼36.0% to ∼3.1% for low-energy (LE) image and ∼29.6% to ∼0.6% for high-energy (HE) image. For step phantom, the SPR is reduced from ∼42.1% and ∼30.3% to ∼3.9% and ∼0.9% for LE and HE image, respectively. The calcification contrast-to-noise ratio is improved by two orders of magnitudes in calcification images. CONCLUSIONS: The proposed method shows an excellent performance on scatter reduction and calcification detection. Compared with hardware based scatter correction strategy, our method need no extra exposure and is easy to implementation.