Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG 2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.Index Terms-Poor visibility environment, object detection, face detection, haze, rain, low-light conditions *The first two authors Wenhan Yang and Ye Yuan contributed equally. Ye Yuan and Wenhan Yang helped prepare the dataset proposed for the UG2+ Challenges, and were the main responsible members for UG2+ Challenge 2019 (Track 2) platform setup and technical support. Wenqi Ren, Jiaying Liu, Walter J. Scheirer, and Zhangyang Wang were the main organizers of the challenge and helped prepare the dataset, raise sponsors, set up evaluation environment, and improve the technical submission. Other authors are the group members of winner teams in UG2+ challenge Track 2 contributing to the winning methods.

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Globally, ovarian cancer (OC) is the leading cause of gynecological cancer-associated deaths. Metastasis, especially multi-organ metastasis, determines the speed of disease progression. A multicenter retrospective study was performed to identify the factors that drive metastasis, from medical records of 534 patients with OC. The average number of target organs per patient was 3.66, indicating multi-organ metastasis. The most common sites of metastasis were large intestine and greater omentum, which were prone to co-metastasis. Results indicated that ascites and laterality, rather than age and menopausal status, were the potential drivers for multi-organ metastasis. Cancer antigen (CA) 125 (CA-125) and nine other blood indicators were found to show a significant, but weak correlation with multi-organ metastasis. A neural network cascade-multiple linear regression hybrid model was built to create an ovarian cancer metastasis index (OCMI) by integration of six multi-organ metastasis drivers including CA-125, blood platelet count, lymphocytes percentage, prealbumin, ascites, and laterality. In an independent set of 267 OC medical records, OCMI showed a moderate correlation with multi-organ metastasis (Spearman ρ = 0.67), the value being 0.72 in premenopausal patients, and good performance in identifying multi-organ metastasis (area under the receiver operating characteristic curve = 0.832), implying a potential prognostic marker for OC.

Background: Traditional Chinese medicine (TCM) is widely integrated into cancer care in China. An overview in 2011 identified 2384 randomized and non-randomized controlled trials (RCTs, non-RCTs) on TCM for cancer published in the Chinese literature. This article summarizes updated evidence of RCTs on TCM for cancer care. Methods: We searched 4 main Chinese databases: China National Knowledge Infrastructure, Chinese Scientific Journal Database, SinoMed, and Wanfang. RCTs on TCM used in cancer care were analyzed in this bibliometric study. Results: Of 5834 RCTs (477 157 cancer patients), only 62 RCTs were indexed in MEDLINE. The top 3 cancers treated were lung, stomach, and breast cancer. About 4752 RCTs (81.45%) tested TCM combined with conventional treatment, and 1082 RCTs (18.55%) used TCM alone for treating symptoms and side-effects. Herbal medicine was the most frequently used TCM modality (5087 RCTs; 87.20%). The most frequently reported outcome was symptom improvement (3712 RCTs; 63.63%) followed by quality of life (2725 RCTs; 46.71%), and biomarkers (2384 RCTs; 40.86%). The majority of RCTs (4051; 69.44%) concluded there were beneficial effects using either TCM alone or TCM plus conventional treatment compared with conventional treatment. Conclusion: Substantial randomized trials demonstrated different types/stages of cancer were treated by various TCM modalities, alone or in combination with conventional medicine. Further evaluation on the effects and safety of TCM modalities focusing on outcomes such as quality of life is required.

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