Weakly Semi-supervised Tool Detection in Minimally Invasive Surgery Videos

no code implementations • 5 Jan 2024 • Ryo Fujii, Ryo Hachiuma, Hideo Saito

We further propose a co-occurrence loss, which considers a characteristic that some tool pairs often co-occur together in an image to leverage image-level labels.

Surgical tool detection

Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge

no code implementations • 11 May 2023 • Aneeq Zia, Kiran Bhattacharyya, Xi Liu, Max Berniker, Ziheng Wang, Rogerio Nespolo, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Bo Liu, David Austin, Yiheng Wang, Michal Futrega, Jean-Francois Puget, Zhenqiang Li, Yoichi Sato, Ryo Fujii, Ryo Hachiuma, Mana Masuda, Hideo Saito, An Wang, Mengya Xu, Mobarakol Islam, Long Bai, Winnie Pang, Hongliang Ren, Chinedu Nwoye, Luca Sestini, Nicolas Padoy, Maximilian Nielsen, Samuel Schüttler, Thilo Sentker, Hümeyra Husseini, Ivo Baltruschat, Rüdiger Schmitz, René Werner, Aleksandr Matsun, Mugariya Farooq, Numan Saaed, Jose Renato Restom Viera, Mohammad Yaqub, Neil Getty, Fangfang Xia, Zixuan Zhao, Xiaotian Duan, Xing Yao, Ange Lou, Hao Yang, Jintong Han, Jack Noble, Jie Ying Wu, Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Herag Arabian, Ning Ding, Knut Moeller, Weiliang Chen, Quan He, Muhammad Bilal, Taofeek Akinosho, Adnan Qayyum, Massimo Caputo, Hunaid Vohra, Michael Loizou, Anuoluwapo Ajayi, Ilhem Berrou, Faatihah Niyi-Odumosu, Lena Maier-Hein, Danail Stoyanov, Stefanie Speidel, Anthony Jarc

Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.

Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder

1 code implementation • 7 Apr 2023 • Mana Masuda, Ryo Hachiuma, Ryo Fujii, Hideo Saito, Yusuke Sekikawa

We propose a deep variational autoencoder-based unsupervised anomaly detection network adapted to the 3D point cloud and an anomaly score specifically for 3D point clouds.

Unsupervised Anomaly Detection

A Two-Block RNN-based Trajectory Prediction from Incomplete Trajectory

no code implementations • 14 Mar 2022 • Ryo Fujii, Jayakorn Vongkulbhisal, Ryo Hachiuma, Hideo Saito

However, most works rely on a key assumption that each video is successfully preprocessed by detection and tracking algorithms and the complete observed trajectory is always available.

Imputation Trajectory Prediction +1

Neural Implicit Event Generator for Motion Tracking

no code implementations • 6 Nov 2021 • Mana Masuda, Yusuke Sekikawa, Ryo Fujii, Hideo Saito

Our framework use pre-trained event generation MLP named implicit event generator (IEG) and does motion tracking by updating its state (position and velocity) based on the difference between the observed event and generated event from the current state estimate.

Position

RGB-D Image Inpainting Using Generative Adversarial Network with a Late Fusion Approach

no code implementations • 14 Oct 2021 • Ryo Fujii, Ryo Hachiuma, Hideo Saito

We expand conventional image inpainting method to RGB-D image inpainting to jointly restore the texture and geometry of missing regions from a pair of RGB and depth images.

Generative Adversarial Network Image Inpainting +3

PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents

1 code implementation • COLING 2020 • Ryo Fujii, Masato Mita, Kaori Abe, Kazuaki Hanawa, Makoto Morishita, Jun Suzuki, Kentaro Inui

Neural Machine Translation (NMT) has shown drastic improvement in its quality when translating clean input, such as text from the news domain.

Machine Translation NMT +1

Nonparametric Bayesian Semi-supervised Word Segmentation

no code implementations • TACL 2017 • Ryo Fujii, Ryo Domoto, Daichi Mochihashi

This paper presents a novel hybrid generative/discriminative model of word segmentation based on nonparametric Bayesian methods.

Language Modelling Machine Translation +2