Yan Wen

Kai Armstrong, Lei Zhang, Yan Wen et al. · microsoft-research

In recent years the NHS has been having increased difficulty seeing all low-risk patients, this includes but not limited to suspected osteoarthritis (OA) patients. To help address the increased waiting lists and shortages of staff, we propose a novel method of automated biomarker identification for diagnosis of knee disorders and the monitoring of treatment progression. The proposed method allows for the measurement and analysis of biomechanics and analyse their clinical significance, in both a cheap and sensitive alternative to the currently available commercial alternatives. These methods and results validate the capabilities of standard RGB cameras in clinical environments to capture motion and show that when compared to alternatives such as depth cameras there is a comparable accuracy in the clinical environment. Biomarker identification using Principal Component Analysis (PCA) allows the reduction of the dimensionality to produce the most representative features from motion data, these new biomarkers can then be used to assess the success of treatment and track the progress of rehabilitation. This was validated by applying these techniques on a case study utilising the exploratory use of local anaesthetic applied on knee pain, this allows these new representative biomarkers to be validated as statistically significant (p-value < 0.05).

Yan Wen, Lei Zhang, Xiangli Meng et al.

Besides the complex nature of colonoscopy frames with intrinsic frame formation artefacts such as light reflections and the diversity of polyp types/shapes, the publicly available polyp segmentation training datasets are limited, small and imbalanced. In this case, the automated polyp segmentation using a deep neural network remains an open challenge due to the overfitting of training on small datasets. We proposed a simple yet effective polyp segmentation pipeline that couples the segmentation (FCN) and classification (CNN) tasks. We find the effectiveness of interactive weight transfer between dense and coarse vision tasks that mitigates the overfitting in learning. And It motivates us to design a new training scheme within our segmentation pipeline. Our method is evaluated on CVC-EndoSceneStill and Kvasir-SEG datasets. It achieves 4.34% and 5.70% Polyp-IoU improvements compared to the state-of-the-art methods on the EndoSceneStill and Kvasir-SEG datasets, respectively.