研究集会題目： 「人工知能の現在と、次世代への実用化」
ーー医療画像に対する有効な解析手法の開発に向けてーー
日時：2018 年 8 月 29 日（水）10 時 00 分～17 時 00 分
場所：明治大学先端数理科学インスティテュート

6 階 603 室

Abstract
中根和昭 「病理画像に対する位相幾何学的概念を用いた解析法」

ホモロジーとは位相幾何学という数学分野の重要な概念のひとつである。現在は高度に抽象化されており、初学者が全てを理解するのには非常に困難である。しかし、二次元の場合（画像解析）に応用する場合は、非常に単純である。この概念の源流は「ケーニヒスベ
ルクの橋の問題」（一筆書き）に端を発しており、接触の状態を定量評価する指標とも解釈できる。今回、ホモロジーを用いた画像解析法を紹介し、病理画像をはじめ多くの医療画像への応用を示す。
KazuakiNakane“ Analyticalmethod

usingtopologicalconcepts

onpathological images”
Homology is one of the important concepts of mathematics field called topology
geometry.
Currently it is highly abstracted and very difficult to understand everything to theperson not specialized in mathematics.
However, in the case of two dimensions (apply to the image analysis), it is very simple.
Because the origin of this concept comes from "Konigsberg's bridge problem" (A strokeof writing), we can interpret as an index for quantitatively evaluating the stateof contact.
This time, image analysis method using homology will be introduced and several
applications to medical images.
小林泰之（聖マリアンナ医科大学）

Imaging Now in Kanagawa
Luis Diago（ MeijiUniv.）

A study on data acquisition and its processing to construct a rating systemof diagnosis ability
藤田広志（岐阜大学）

Current status and future of computer-aided diagnosis (CAD) in clinicalimaging in the new era of artificial intelligence (AI)Computer-aided detection/diagnosis, so-called "CAD", is rapidly entering theradiology mainstream. It has already become a part of the routine clinical work
especially for the detection of breast cancer with mammograms/ultrasound, chest
nodule with radiography/CT images, and polyp with CT colonography, in which thecomputer output is used as a "Second Opinion" in assisting radiologists' imageinterpretations. Recent powerful AI technology such as deep learning technology
advances the development and improving the performance of CAD to the next stage, andsometimes it is called as "AI-CAD". In my talk, current status and its future of AI-CAD
will be introduced and some issues to be solved will be also be discussed.
Jun Xu (Nanjin Unv.)
Computational Pathology for Precision Medicine
Computational pathology is the integration of digital pathology with advanced
artificial intelligence (e.g., machine learning) technology. Its goal is to use acombination of primary sources of data (e.g., pathology, radiology, clinicalelectronic medical records, laboratory data, etc.) to achieve more accurate diseasediagnosis and optimal clinical care. My talk comprises the following four aspects:1) what is the computational pathology and how this technology will change thepathologists’ role in clinical care; how computational pathology will contributeto precision medicine. 2) our recent works in developing advanced deep learning based
approaches for the histopathological image analysis in the cells and tissues levelcomputation; 3) Based on the cells and tissues level computation, PathOmics weredeveloped towards

tumorquantificationand precision diseasediagnosisandprognosis; 4) Our recent work on the fusion of radiological and pathological data
for more accurate diagnosis and prognosis.
Nasir

Rajpoot (Warwick Univ.)

Histology Footprint Analytics
The human brain is fantastic at recognizing people and objects and building an
understanding of the natural world around us. However, the visual cortex is not idealat objectively measuring
what we see and complex spatial patterns hidden in plain sight cannot sometimes be
deciphered by the naked eye. Computational Pathology is an emerging discipline
concerned with the study of computer algorithms for understanding disease from theanalysis of digitised histology images. I will show some snippets of computational

pathology research in my group to demonstrate the value of analytics ofinformation-rich, high-resolution whole-slide images (WSIs, the so-called Big Cancer
Image Data) for cancer diagnosis and prognosis. I will show examples of howhistological motifs extracted from digital pathology image data are likely to leadto patient stratification for precision medicine. I will then discuss some of themain challenges in digital pathology and opportunities for exploring new unchartered
territories.