103 research outputs found
Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training
Universal lesion detection has great value for clinical practice as it aims
to detect various types of lesions in multiple organs on medical images. Deep
learning methods have shown promising results, but demanding large volumes of
annotated data for training. However, annotating medical images is costly and
requires specialized knowledge. The diverse forms and contrasts of objects in
medical images make fully annotation even more challenging, resulting in
incomplete annotations. Directly training ULD detectors on such datasets can
yield suboptimal results. Pseudo-label-based methods examine the training data
and mine unlabelled objects for retraining, which have shown to be effective to
tackle this issue. Presently, top-performing methods rely on a dynamic
label-mining mechanism, operating at the mini-batch level. However, the model's
performance varies at different iterations, leading to inconsistencies in the
quality of the mined labels and limits their performance enhancement. Inspired
by the observation that deep models learn concepts with increasing complexity,
we introduce an innovative exploratory training to assess the reliability of
mined lesions over time. Specifically, we introduce a teacher-student detection
model as basis, where the teacher's predictions are combined with incomplete
annotations to train the student. Additionally, we design a prediction bank to
record high-confidence predictions. Each sample is trained several times,
allowing us to get a sequence of records for each sample. If a prediction
consistently appears in the record sequence, it is likely to be a true object,
otherwise it may just a noise. This serves as a crucial criterion for selecting
reliable mined lesions for retraining. Our experimental results substantiate
that the proposed framework surpasses state-of-the-art methods on two medical
image datasets, demonstrating its superior performance
Who gets hired by top LIS schools in China?
In this study, we provide evidence and discuss findings regarding talent flow and intellectual diversity in library and information science (LIS) using a faculty hiring network of 274 full-time faculty members from 7 top LIS schools in China. We employ three groups of data items, including the universities they got Ph.D., their Ph.D. programs, and whether their graduation schools are iSchools. We use these to develop a descriptive analysis of the community's educational backgrounds. We show that faculty members in Nankai University are the most diverse, while Wuhan University, Nanjing University, Renmin University of China, and Peking University are experiencing intellectual inbreeding. Wuhan University has sent the largest number of talents to other LIS schools. Top LIS schools in China prefers those who graduated from LIS schools and more than half of the faculty members at each of the top 7 LIS schools graduated from iSchools. Overall, LIS faculty educational backgrounds analysis has considerable value in deriving credible academic hiring and revealing talent flow within the field
Robust saliency detection via regularized random walks ranking
In the field of saliency detection, many graph-based algorithms heavily depend on the accuracy of the pre-processed superpixel segmentation, which leads to significant sacrifice of detail information from the input image. In this paper, we propose a novel bottom-up saliency detection approach that takes advantage of both region-based features and image details. To provide more accurate saliency estimations, we first optimize the image boundary selection by the proposed erroneous boundary removal. By taking the image details and region-based estimations into account, we then propose the regularized random walks ranking to formulate pixel-wised saliency maps from the superpixel-based background and foreground saliency estimations. Experiment results on two public datasets indicate the significantly improved accuracy and robustness of the proposed algorithm in comparison with 12 state-of-the-art saliency detection approaches
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