湯本 高行

本研究では，事故の概要を説明したテキストを入力として，当事者が受けた傷病の程度を推定する手法を提案する．入力の対象とするテキストは，数文程度の文書を想定している．機械学習による分類問題を解くことで，その入力に該当する傷病の程度を推定するというのが提案手法の構成となる．本研究で利用するデータは，事故情報データバンクシステムで公開されている事故データである．入力として用いるのは「事故の概要」項目に記載されたテキストである．提案手法では，入力テキストを汎用言語モデルBERTを利用して分散表現として表現する．BERTのモデルとしては，日本語Wikipediaを用いて学習された事前学習モデルを用いる．しかし，傷病の程度を推定するというタスクの正解率を向上させるために，四つの工夫，（1）クラスウェイト，（2）Ordinal Classification，（3）マルチタスクラーニング，（4）トークンラベル推定による追加学習モデル，を導入する．これらの工夫を用いる場合と用いない場合において，傷病の程度の推定の正解率やMacro F1，RMSE，混同行列による評価にどのような影響が出るかを検証した．その結果，（1）クラスウェイト，並びに，（2）Ordinal Classificationを導入した際に，Macro F1の向上とRMSEの改善が得られるという結果となった．また，（3）マルチタスクラーニングを導入した際に正解率の向上が見られた．

In this paper, a support-vector-machine(SVM)- based method of detecting stenosis is presented for fallopian tubal models. It copes with stenosis detection as classification of data prepared from results of ultrasonic measurements conducted for tubal models. Under assumption that waves reflected at the second and third boundary surfaces of the models potentially include characteristics associated with blocked sections (i.e., stenosis), the method determines the time range having the reflected waves, by referring to maximal values on envelope curves of them The determined range is divided into regular short intervals, and the difference between maximum value and minimum value on envelope curves is calculated for each interval. The ten-dimensional data used to SVM learning and stenosis detection is prepared from the frequency distribution of the number of the short intervals versus difference values. Experimental results establish that the method can achieves favorable accuracies in checking occurrence of stenosis and in identifying tubal model types.

In this paper, a method of achieving high accuracy in discriminating right and left eyes is proposed for ophthalmic surgery. A VGG16 convolutional neural network is employed to construct a main classifier. The data presented to the main classifier are some frames sampled at regular intervals from surgery videos. Before classifying the frames to be examined, the proposed method determines whether they are suitable or not to improve the discrimination accuracy as high as possible. In other words, the frames causing erroneous discrimination are omitted. The determination of frames depends on image characteristics associated with lightness values and edges, and on positions of eyelid speculums in them. It is based on SegNet neural networks. The proposed method determines the frames to be presented, if rectangular areas specified by bent parts of the speculums adequately appear in the predefined regions inside them. Experimental results reveal that the proposed method achieves the favorable discrimination accuracy with a small number of data in training SegNet networks compared with another method.

In this paper, a method of determining examinations is proposed for ophthalmologic outpatients, using feedforward neural network (NN for short). The determination is based on the data classification. The proposed method defines four classes of ophthalmologic examinations. It prepares data for NN training and examination determination from handwriting sentences in outpatients' interview sheets. A set of the training data is prepared in the form of a matrix. The words extracted from the sentences are assigned to the matrix columns, while each sheet (or sentences in it) is assigned to a matrix rows. Entries in the matrix takes binary values meaning whether extracted words appear in the sentences in the sheet. The proposed method also the ages of outpatients as entries. NN training is conducted according to the normal backpropagation algorithm using a row as one of the training data. The trained NN has four output neurons each of which takes the value belonging to the range [0, 1]. The class of data to be examined is determined by searching the neuron at which the largest value appears in the output layer. Experimental results comprehensively establish that the proposed method can achieve higher percentages of concordance than other methods.