Breast Tumor Tissue Image Classification Using Single-Task Meta Learning with Auxiliary Network.

Breast cancer has a high mortality rate among cancers. If the type of breast tumor can be correctly diagnosed at an early stage, the survival rate of the patients will be greatly improved. Considering the actual clinical needs, the classification model of breast pathology images needs to have the ability to make a correct classification, even in facing image data with different characteristics. The existing convolutional neural network (CNN)-based models for the classification of breast tumor pathology images lack the requisite generalization capability to maintain high accuracy when confronted with pathology images of varied characteristics. Consequently, this study introduces a new classification model, STMLAN (Single-Task Meta Learning with Auxiliary Network), which integrates Meta Learning and an auxiliary network. Single-Task Meta Learning was proposed to endow the model with generalization ability, and the auxiliary network was used to enhance the feature characteristics of breast pathology images. The experimental results demonstrate that the STMLAN model proposed in this study improves accuracy by at least 1.85% in challenging multi-classification tasks compared to the existing methods. Furthermore, the Silhouette Score corresponding to the features learned by the model has increased by 31.85%, reflecting that the proposed model can learn more discriminative features, and the generalization ability of the overall model is also improved.

Breast Tumor Tissue Image Classification Using DIU-Net.

Inspired by the observation that pathologists pay more attention to the nuclei regions when analyzing pathological images, this study utilized soft segmentation to imitate the visual focus mechanism and proposed a new segmentation-classification joint model to achieve superior classification performance for breast cancer pathology images. Aiming at the characteristics of different sizes of nuclei in pathological images, this study developed a new segmentation network with excellent cross-scale description ability called DIU-Net. To enhance the generalization ability of the segmentation network, that is, to avoid the segmentation network from learning low-level features, we proposed the Complementary Color Conversion Scheme in the training phase. In addition, due to the disparity between the area of the nucleus and the background in the pathology image, there is an inherent data imbalance phenomenon, dice loss and focal loss were used to overcome this problem. In order to further strengthen the classification performance of the model, this study adopted a joint training scheme, so that the output of the classification network can not only be used to optimize the classification network itself, but also optimize the segmentation network. In addition, this model can also provide the pathologist model's attention area, increasing the model's interpretability. The classification performance verification of the proposed method was carried out with the BreaKHis dataset. Our method obtains binary/multi-class classification accuracy 97.24/93.75 and 98.19/94.43 for 200× and 400× images, outperforming existing methods.

Stain Style Transfer for Histological Images Using S3CGAN.

This study proposes a new CycleGAN-based stain transfer model, called S3CGAN, equipped with a specialized color classifier structure. The specialized color classifier can assist the generative network to conquer the existing challenge in GANs, namely the instability of the network caused by the insufficient representativeness of the training data in the initial stage of network training. The color classifier is pretrained, hence it can provide correct color information feedback to the generator during the initial network training phase. The augmented information from color classification enables the generator to generate superior results. Owing to the CycleGAN architecture, the proposed model does not require representative paired inputs. The proposed model uses U-Net and a Markovian discriminator to enhance the structural retention ability to generate images with high fidelity.

A visual context-awareness-based sleeping-respiration measurement system.

Due to the rapid growth of the elderly population, improving specific aspects of elderly healthcare has become more important. Sleeping care systems for the elderly are rare. In this paper, we propose a visual context-aware-based sleeping-respiration measurement system that measures the respiration information of elderly sleepers. Accurate respiration measurement requires considering all possible contexts for the sleeping person. The proposed system consists of a body-motion-context-detection subsystem, a respiration-context-detection subsystem, and a fast motion-vector-estimation-based respiration measurement subsystem. The system yielded accurate respiratory measurements for our study population.

A real time collaboration system for teleradiology consultation.

Real time collaboration systems, in which participants share multimedia data and applications in real time, have attracted many researchers in recent years. A teleradiology consultation system based on the real time collaboration technology is presented in this paper. Under the platform-independence consideration, Java technologies are employed to construct the system. Applying this system, an off-duty on-call radiologist can make diagnoses and report easily by viewing the transferred images at home. Owing to the accessibility of image, all users can examine and manipulate images consistently such that a secluded hospital can be assisted to hold remote consultation. To reduce the network transmission time, the command-passing and local command execution techniques are utilized to achieve the screen synchronization. A pointer function is also developed to maintain the cursor consistency in a more efficient manner during consultation when a detail indication of the examined image is needed. Besides, a dialog window is also designed for on-line conversation. Since Java programs can run on heterogeneous platforms, the need for system maintenance and user training can be substantially reduced.