Early detection and segmentation of skin lesions is crucial for timely
diagnosis and treatment, necessary to improve the survival rate of patients.
However, manual delineation is time consuming and subject to intra- and
inter-observer variations among dermatologists. This underlines the need for an
accurate and automatic approach to skin lesion segmentation. To tackle this
issue, we propose a multi-task convolutional neural network (CNN) based, joint
detection and segmentation framework, designed to initially localize the lesion
and subsequently, segment it. A `Faster region-based convolutional neural
network' (Faster-RCNN) which comprises a region proposal network (RPN), is used
to generate bounding boxes/region proposals, for lesion localization in each
image. The proposed regions are subsequently refined using a softmax classifier
and a bounding-box regressor. The refined bounding boxes are finally cropped
and segmented using `SkinNet', a modified version of U-Net. We trained and
evaluated the performance of our network, using the ISBI 2017 challenge and the
PH2 datasets, and compared it with the state-of-the-art, using the official
test data released as part of the challenge for the former. Our approach
outperformed others in terms of Dice coefficients ($>0.93$), Jaccard index
($>0.88$), accuracy ($>0.96$) and sensitivity ($>0.95$), across five-fold cross
validation experiments.

使用 Faster-RCNN 和 SkinNet 的多任务卷积神经网络（CNN）的联合检测和分割框架，对皮肤病变进行准确和自动地划分，并且在 ISBI 2017 数据集上得到了 Dice 系数大于 0.93、Jaccard 系数大于 0.88、准确率大于 0.96 和敏感度大于 0.95 的良好表现。

皮肤病损检测和分割的多任务框架

A Multi-task Framework for Skin Lesion Detection and Segmentation

Automatic segmentation of liver lesions is a fundamental requirement towards
the creation of computer aided diagnosis (CAD) and decision support systems
(CDS). Traditional segmentation approaches depend heavily upon hand-crafted
features and a priori knowledge of the user. As such, these methods are
difficult to adopt within a clinical environment. Recently, deep learning
methods based on fully convolutional networks (FCNs) have been successful in
many segmentation problems primarily because they leverage a large labelled
dataset to hierarchically learn the features that best correspond to the
shallow visual appearance as well as the deep semantics of the areas to be
segmented. However, FCNs based on a 16 layer VGGNet architecture have limited
capacity to add additional layers. Therefore, it is challenging to learn more
discriminative features among different classes for FCNs. In this study, we
overcome these limitations using deep residual networks (ResNet) to segment
liver lesions. ResNet contain skip connections between convolutional layers,
which solved the problem of the training degradation of training accuracy in
very deep networks and thereby enables the use of additional layers for
learning more discriminative features. In addition, we achieve more precise
boundary definitions through a novel cascaded ResNet architecture with
multi-scale fusion to gradually learn and infer the boundaries of both the
liver and the liver lesions. Our proposed method achieved 4th place in the ISBI
2017 Liver Tumor Segmentation Challenge by the submission deadline.

本研究采用一种基于深度残余网络 (ResNet) 的肝脏病变分割方法，通过级联 ResNet 架构和多尺度融合生成更精确的边界定义，该方法在 ISBI 2017 肝脏瘤分割挑战中获得第四名。