Uncertainty Ordinal Multi-Instance Learning for Breast Cancer Diagnosis.

Ordinal multi-instance learning (OMIL) deals with the weak supervision scenario wherein instances in each training bag are not only multi-class but also have rank order relationships between classes, such as breast cancer, which has become one of the most frequent diseases in women. Most of the existing work has generally been to classify the region of interest (mass or microcalcification) on the mammogram as either benign or malignant, while ignoring the normal mammogram classification. Early screening for breast disease is particularly important for further diagnosis. Since early benign lesion areas on a mammogram are very similar to normal tissue, three classifications of mammograms for the improved screening of early benign lesions are necessary. In OMIL, an expert will only label the set of instances (bag), instead of labeling every instance. When labeling efforts are focused on the class of bags, ordinal classes of the instance inside the bag are not labeled. However, recent work on ordinal multi-instance has used the traditional support vector machine to solve the multi-classification problem without utilizing the ordinal information regarding the instances in the bag. In this paper, we propose a method that explicitly models the ordinal class information for bags and instances in bags. Specifically, we specify a key instance from the bag as a positive instance of bags, and design ordinal minimum uncertainty loss to iteratively optimize the selected key instances from the bags. The extensive experimental results clearly prove the effectiveness of the proposed ordinal instance-learning approach, which achieves 52.021% accuracy, 61.471% sensitivity, 47.206% specificity, 57.895% precision, and an 59.629% F1 score on a DDSM dataset.

Response mechanism of ♀ Epinephelus fuscoguttatus × â™‚ Epinephelus lanceolatus under low-temperature and waterless stresses using TMT proteomic analysis.

♀Epinephelus fuscoguttatus × â™‚Epinephelus lanceolatus, a hybrid grouper created from artificial breeding, has been widely developed over the past decades. However, the study focusing on lukewarm high-protein-content fish species using advanced techniques has rarely been reported. In this work, the TMT (tandem mass tag)-assisted technique was employed to explore its differentially expressed proteins and response mechanisms under low-temperature dormant and waterless stresses. Our findings suggest that 162 and 258 differentially expressed proteins were identified under low-temperature dormant and waterless stresses, respectively. The waterless preservation treatment further identifies 93 differentially expressed proteins. The identified proteins are categorized and found to participate in lipid metabolism, glycometabolism, oxidative stress, immune response, protein and amino acid metabolism, signal transduction, and other functions. Accordingly, the factors that affect the response mechanisms are highlighted to provide new evidences at protein level.