Gang Xu

Xuefeng Jiang, Jia Li, Nannan Wu et al.

Robustness to label noise within data is a significant challenge in federated learning (FL). From the data-centric perspective, the data quality of distributed datasets can not be guaranteed since annotations of different clients contain complicated label noise of varying degrees, which causes the performance degradation. There have been some early attempts to tackle noisy labels in FL. However, there exists a lack of benchmark studies on comprehensively evaluating their practical performance under unified settings. To this end, we propose the first benchmark study FNBench to provide an experimental investigation which considers three diverse label noise patterns covering synthetic label noise, imperfect human-annotation errors and systematic errors. Our evaluation incorporates eighteen state-of-the-art methods over five image recognition datasets and one text classification dataset. Meanwhile, we provide observations to understand why noisy labels impair FL, and additionally exploit a representation-aware regularization method to enhance the robustness of existing methods against noisy labels based on our observations. Finally, we discuss the limitations of this work and propose three-fold future directions. To facilitate related communities, our source code is open-sourced at https://github.com/Sprinter1999/FNBench.

Gang Xu, Zhigang Song, Zhuo Sun et al.

Histopathology image analysis plays a critical role in cancer diagnosis and treatment. To automatically segment the cancerous regions, fully supervised segmentation algorithms require labor-intensive and time-consuming labeling at the pixel level. In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels. Using multiple instance learning (MIL)-based label enrichment, CAMEL splits the image into latticed instances and automatically generates instance-level labels. After label enrichment, the instance-level labels are further assigned to the corresponding pixels, producing the approximate pixel-level labels and making fully supervised training of segmentation models possible. CAMEL achieves comparable performance with the fully supervised approaches in both instance-level classification and pixel-level segmentation on CAMELYON16 and a colorectal adenoma dataset. Moreover, the generality of the automatic labeling methodology may benefit future weakly supervised learning studies for histopathology image analysis.

Xiaoshuo Li, Tiezhu Yue, Xuanping Huang et al.

Homework grading is critical to evaluate teaching quality and effect. However, it is usually time-consuming to grade the homework manually. In automatic homework grading scenario, many optical mark reader (OMR)-based solutions which require specific equipments have been proposed. Although many of them can achieve relatively high accuracy, they are less convenient for users. In contrast, with the popularity of smart phones, the automatic grading system which depends on the image photographed by phones becomes more available. In practice, due to different photographing angles or uneven papers, images may be distorted. Moreover, most of images are photographed under complex backgrounds, making answer areas detection more difficult. To solve these problems, we propose BAGS, an automatic homework grading system which can effectively locate and recognize handwritten answers. In BAGS, all the answers would be written above the answer area underlines (AAU), and we use two segmentation networks based on DeepLabv3+ to locate the answer areas. Then, we use the characters recognition part to recognize students' answers. Finally, the grading part is designed for the comparison between the recognized answers and the standard ones. In our test, BAGS correctly locates and recognizes the handwritten answers in 91% of total answer areas.