Takumi Ishikawa

Gastric Lymph Node Cancer Detection Using Multiple Features Support Vector Machine for Pathology Diagnosis Support System

In this paper, an automatic cancer detection method that combines multiple features to support pathologists is presented. Cancer is the most cause of death in Japan, and patients suffering with and who die of cancer are increasing every year, while the number of pathologists is almost constant. Such issues increase the burden on the pathologists and causes service degradation for the patients. One of the ways which resolve these pathologists’ issues is a double checking by pathologists and systems. The method was proposed for detecting cancer in the Pathology diagnosis support system to introduce a double checking. The proposed method combined three image features, Higher-order Local Auto-Correlation (HLAC) feature, Wavelet feature, Delaunay feature, in varying weights. At first, the features was calculated from HE stained gastric lymph node images. We connected each feature into one vector of varying combinations of the features, and discriminate cancer and no cancer by Support Vector Machine (SVM). Cancer detection rates with most combinations of more than two features were better than just one feature. In addition, by changing the scale of Delaunay in 35-order HLAC, Delaunay and Wavelet combination vector, sensitivity was improved. In the best performance, sensitivity and specificity were 95.7% and 82.1% respectively. Therefore, the proposed method can be used for a double check system.

Beyond Supporting Pathological Diagnosis: Concept of Support System for Pathologist and Researcher

This paper presents the Support System for auto-Pathological Diagnosis (P-SSD) to support pathologists and researchers of auto-pathological diagnosis. P-SSD has two main contributions. First, P-SSD detects cancer area with machine learning methods, and then shows cancer areas to pathologists. The pathologists can make diagnosis with the detected result, which reduce the burden on pathologists. Second, P-SSD is an open software platform for pathological image too. The platform offers some basic functionalities to load and display pathological images, and implement the image-processing methods though plugins. It helps researchers to apply feature calculation and machine leaning techniques to the pathological images effortlessly. In conclusion, P-SSD is the software with potential applications in both of pathological diagnosis and research.

Gastric Lymph Node Cancer Detection of Multiple Features Classifier for Pathology Diagnosis Support System

In this paper, an automatic cancer detection method that combines multiple features to support pathologists was proposed. Cancer is the most cause of death in Japan, and patients suffering with cancer are increasing every year, while the number of pathologists is almost constant. Such issues increase the burden on the pathologists and causes service degradation for the patients. The proposed method combined three image features, Higher-order Local Auto-Correlation (HLAC) feature, Wavelet feature, Delaunay feature. At first, the features were calculated from gastric lymph node images. Then we connected each feature into each vector of varying combinations of the features, and discriminated cancer and no cancer by Support Vector Machine (SVM). HLAC, Wavelet and Delaunay features are shape, frequency, and cell-position geometrical one respectively. Cancer detection rates with more than two features combination were better than only one. In the best performance, sensitivity and specificity were 94.6% and 84.9% respectively.

Support system for pathologists and researchers

Aims: In Japan, cancer is the most prevalent cause of death; the number of patients suffering from cancer is increasing. Hence, there is an increased burden on pathologists to make diagnoses. To reduce pathologists′ burden, researchers have developed methods of auto-pathological diagnosis. However, virtual slides, which are created when glass slides are digitally scanned, saved in a unique format, and it is difficult for researchers to work on the virtual slides for developing their own image processing method. This paper presents the support system for pathologists and researchers who use auto-pathological diagnosis (P-SSD). Main purpose of P-SSD was to support both of pathologists and researchers. P-SSD consists of several sub-functions that make it easy not only for pathologists to screen pathological images, double-check their diagnoses, and reduce unimportant image data but also for researchers to develop and apply their original image-processing techniques to pathological images. Methods: We originally developed P-SSD to support both pathologists and researchers developing auto-pathological diagnoses systems. Current version of P-SSD consists of five main functions as follows: (i) Loading virtual slides, (ii) making a supervised database, (iii) learning image features, (iv) detecting cancerous areas, (v) displaying results of detection. Results: P-SSD reduces computer memory size random access memory utilization and the processing time required to divide the virtual slides into the smaller-size images compared with other similar software. The maximum observed reduction in computer memory size and reduction in processing time is 97% and 99.94%, respectively. Conclusions: Unlike other vendor-developed software, P-SSD has interoperability and is capable of handling virtual slides in several formats. Therefore, P-SSD can support both of pathologists and researchers, and has many potential applications in both pathological diagnosis and research area.