The accurate identification of malignant lung nodules on chest CT is critical for the early detection of lung cancer, which also offers patients the best chance of cure. Deep learning methods have recently been successfully introduced to computer vision problems, although substantial challenges remain in the detection of malignant nodules due to the lack of large training datasets. In this paper, we propose a multi-view knowledge-based collaborative (MV-KBC) deep model to separate malignant from benign nodules using limited chest CT data. Our model learns 3D lung nodule characteristics by decomposing a 3D nodule into nine fixed views. For each view, we construct a knowledge-based collaborative (KBC) submodel, where three types of image patches are designed to fine-tune three pre-trained ResNet-50 networks that characterize the nodules' overall appearance, voxel and shape heterogeneity, respectively. We jointly use the nine KBC submodels to classify lung nodules with an adaptive weighting scheme learned during the error back propagation, which enables the MV-KBC model to be trained in an end-to-end manner. The penalty loss function is used for better reduction of the false negative rate with a minimal effect on the overall performance of the MV-KBC model. We tested our method on the benchmark LIDC-IDRI dataset and compared it to five state-of-the-art classification approaches. Our results show that the MV-KBC model achieved an accuracy of 91.60% for lung nodule classification with an AUC of 95.70%. These results are markedly superior to the state-of-the-art approaches.

The task of segmenting cell nuclei and cytoplasm in pap smear images is one of the most challenging tasks in automated cervix cytological analysis due to specifically the presence of overlapping cells. This paper introduces a multi-pass fast watershed-based method (MPFW) to segment both nucleus and cytoplasm from large cell masses of overlapping cervical cells in three watershed passes. The first pass locates the nuclei with barrier-based watershed on the gradient-based edge map of a pre-processed image. The next pass segments the isolated, touching, and partially overlapping cells with a watershed transform adapted to the cell shape and location. The final pass introduces mutual iterative watersheds separately applied to each nucleus in the largely overlapping clusters to estimate the cell shape. In MPFW, the line-shaped contours of the watershed cells are deformed with ellipse fitting and contour adjustment to give a better representation of cell shapes. The performance of the proposed method has been evaluated using synthetic, real extended depth-of-field, and multi-layers cervical cytology images provided by the first and second overlapping cervical cytology image segmentation challenges in ISBI 2014 and ISBI 2015. The experimental results demonstrate superior performance of the proposed MPFW in terms of segmentation accuracy, detection rate, and time complexity, compared with recent peer methods.

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Conventional low-level feature based saliency detection methods tend to use non-robust prior knowledge and do not perform well in complex or low-contrast images. In this paper, to address the issues above in existing methods, we propose a novel deep neural network (DNN) based dense and sparse labeling (DSL) framework for saliency detection. DSL consists of three major steps, namely dense labeling (DL), sparse labeling (SL) and deep convolutional (DC) network. The DL and SL steps conduct initial saliency estimations with macro object contours and low-level image features, respectively, which effectively approximate the location of the salient object and generate accurate guidance channels for the DC step; the DC step, on the other hand, takes in the results of DL and SL, establishes a 6-channeled input data structure (including local superpixel information), and conducts accurate final saliency classification. Our DSL framework exploits the saliency estimation guidance from both macro object contours and local low-level features, as well as utilizing the DNN for high-level saliency feature extraction. Extensive experiments are conducted on six well-recognized public datasets against sixteen state-of-the-art saliency detection methods, including ten conventional feature based methods and six learning based methods. The results demonstrate the superior performance of DSL on various challenging cases in terms of both accuracy and robustness.