The classification of whole slide images (WSIs) is crucial in computational pathology and has significant clinical implications. Due to the extremely high resolution of WSIs and the lack of detailed lesion annotations, Multiple instance learning (MIL) has recently shown great promise for WSI classification by modeling WSIs as "bags" and treating cropped patches as "instances". However, using pre-trained feature extractors often leads to biased instance representations as the data used to pre-train these models differ significantly from histopathology data. Furthermore, since focusing on only certain instances may lead to overlooking important details, it is crucial to comprehensively assess the relevance of all instances for positive instance selection. In this paper, we propose a weakly supervised method to enhance WSI classification using adaptive representation and an instance relevance scoring strategy. To address the issue of biased data representation, we introduce an adaptive representation designed to enhance features relevant to lesion regions. This involves an adaptive block that transforms input features to better represent these critical characteristics, while simultaneously applying an attention-based probability distribution to maintain consistency between the transformed features. Additionally, we propose an instance relevance scoring strategy that assigns importance scores to each instance based on its contribution to the classification. Two publicly available datasets, CAMELYON-16 and TCGA-NSCLC, are used to validate the proposed method. The experimental results show that our proposed method outperforms existing state-of-the-art approaches in WSI classification.

Severity grading plays a vitally important role in the diagnosis and treatment of acne. However, due to its special characteristics such as similar samples, unclear class boundaries and imbalanced categories, the diagnosis is extremely easy to be confused. In this paper, we propose a novel, simple and intuitive loss function, namely Aggregation Separation Loss (ASLoss), as an adjunct for classification loss to clarify the common easily-confused cases. The ASLoss mines the commonalities of the same severity and the gaps among different severities in deep feature spaces. To demonstrate the generality of the proposed ASLoss, we also validate ASLoss on another common easily-confused task of expression recognition. The experimental results show that representations extracted by ASLoss are sufficiently clear and distinguishable, the performance of various popular methods can be improved significantly by ASLoss, the optimal network reaches the state-of-the-art and diagnostic level of dermatologists and the ASLoss can be generalized to improve the performance on other easily-confused tasks.

Acne seriously affects people’s daily lives. Several studies of automated acne diagnosis either lack reasonable interpretation to support the diagnosis or ignore evidence in the diagnosis process. In this paper, we propose an interpretable diagnosis framework for face acne. This framework uses complementation learning of evidence localization and severity level grading to boost both streams by sharing the features that support each other. Evidence localization learns to identify lesion areas for supporting the diagnosis stream as well as providing interpretation. Severity level grading learns to recognize the diagnosis result and also provides reference and rectification for evidence localization. Experimental results show that complementation learning improves both evidence localization and severity level grading, the lesion areas from evidence localization can support the diagnosis and provide interpretations, and the diagnosis framework reaches the state-of-the-art level and the diagnostic performance of dermatologists.

Acne Vulgaris seriously affects people’s daily life. In this paper, we propose a face acne grading framework which is a new paradigm to solve the image classification problem where the number and type of small objects are the evidence. This framework includes two components: prior knowledge extraction and prior knowledge guided network. The prior knowledge extraction uses an excellent segmentation method to predict the lesion areas as prior knowledge. The prior knowledge guided network fuses the prior knowledge and its corresponding image to grade the severity. The experiment results demonstrate that our framework achieves the state-of-the-art and diagnosis level of dermatologists.

The collection and publication of medical images on the face are quite difficult because of the invasion of privacy. Meanwhile, it takes a major expenditure of time and effort to manually label large-scale face images covered with s o many fine skin lesions. In this work, a multi-class object large-scale image inpainting model Class-Guided PG-GAN (CGPG-GAN) is proposed and its application in boosting downstream model performances is explored. This model is applied on face acne lesion inpainting where the image size is very large and missing areas are different types of lesions. The experiment results show that our method is superior to some existing methods and can improve the performance of downstream diagnosis remarkably.