Deep Learning in Medical Image Analysis

Cell detection. Digitized tissue histopathology has recently been employed for micro- scopic examination and automatic disease grading. A primary challenge in microscopic image analysis involves the need to analyze all individual cells for accurate diagnosis, given that the dif- ferentiation of most disease grades depends strongly on cell-level information. To address this challenge, researchers have employed deep CNNs to robustly and accurately detect and segment cells from histopathological images (37, 38, 53, 54, 97–99), which can significantly benefit cell-level analysis for cancer diagnosis.

In a pioneering study, Cires¸an et al. (37) used a deep CNN to detect mitosis in breast cancer histology images. Their networks were trained to classify each pixel in the images from a patch cen- tered on the pixel. Their method won the 2012 International Conference on Pattern Recognition (ICPR) Mitosis Detection Contest,⁴ outperforming other contestants by a significant margin.
Since then, different groups have used different deep learning methods for detection in histology images. For example, Xu et al. (54) used an SAE to detect cells on breast cancer histological images. To train their deep model, they utilized a denoising auto-encoder to improve robustness to outliers and noises. Su et al. (53) also used an SAE as well as sparse representation to detect and segment cells from microscopic images. Sirinukunwattana et al. (100) proposed a spatially constrained CNN (SC-CNN) to detect and classify nuclei in histopathology images. Specifically, they used an SC-CNN to estimate the likelihood of a pixel being the center of a nucleus, where pixels with high probability values were spatially constrained to locate in the vicinity of the center of nuclei. They also developed a neighboring ensemble predictor coupled with a CNN to more accurately predict the class label of the detected cell nuclei. Chen et al. (38) designed a deep cascaded CNN by exploiting the technique of the full CNN, which replaces the fully connected layers with all- convolutional kernels (101). They first trained a coarse retrieval model to identify and locate mitosis candidates while maintaining high sensitivity. On the basis of the retrieved candidates, they then created a fine discrimination model by transferring deep and rich feature hierarchies learned on a large natural image data set to distinguish mitoses from hard mimics. Their cascaded CNN achieved the best detection accuracy in the 2014 ICPR MITOS-ATYPIA challenge.⁵

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