R
Richard J. Maude
Researcher at Mahidol University
Publications - 195
Citations - 6071
Richard J. Maude is an academic researcher from Mahidol University. The author has contributed to research in topics: Malaria & Medicine. The author has an hindex of 35, co-authored 160 publications receiving 4471 citations. Previous affiliations of Richard J. Maude include Churchill Hospital & University of Edinburgh.
Papers
More filters
Journal ArticleDOI
Spread of artemisinin-resistant Plasmodium falciparum in Myanmar: a cross-sectional survey of the K13 molecular marker
Kyaw Myo Tun,Mallika Imwong,Khin Maung Lwin,Aye A. Win,Tin Maung Hlaing,Thaung Hlaing,Khin Lin,Myat Phone Kyaw,Katherine Plewes,Katherine Plewes,M. Abul Faiz,Mehul Dhorda,Phaik Yeong Cheah,Phaik Yeong Cheah,Sasithon Pukrittayakamee,Elizabeth A. Ashley,Elizabeth A. Ashley,Tim J. Anderson,Shalini Nair,Marina McDew-White,Jennifer A. Flegg,Eric P. M. Grist,Philippe Allard Guérin,Richard J. Maude,Richard J. Maude,Frank Smithuis,Arjen M. Dondorp,Arjen M. Dondorp,Nicholas P. J. Day,Nicholas P. J. Day,François Nosten,Nicholas J. White,Nicholas J. White,Charles J. Woodrow,Charles J. Woodrow +34 more
TL;DR: Assessment of the spread of artemisinin-resistant P falciparum in Myanmar finds Artemisinin resistance extends across much of Myanmar, and Appropriate therapeutic regimens should be tested urgently and implemented comprehensively if spread of warfarin resistance to other regions is to be avoided.
Journal ArticleDOI
Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
Sivaramakrishnan Rajaraman,Sameer Antani,Mahdieh Poostchi,Kamolrat Silamut,Md. Amir Hossain,Richard J. Maude,Richard J. Maude,Richard J. Maude,Stefan Jaeger,George R. Thoma +9 more
TL;DR: This study evaluates the performance of pre-trained CNN based DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening and experimentally determines the optimal model layers for feature extraction from the underlying data.
Journal ArticleDOI
Image analysis and machine learning for detecting malaria
TL;DR: The different approaches published in the literature are organized according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification for microscopic malaria diagnosis.
Journal ArticleDOI
Determinants of dihydroartemisinin-piperaquine treatment failure in Plasmodium falciparum malaria in Cambodia, Thailand, and Vietnam: a prospective clinical, pharmacological, and genetic study
Rob W. van der Pluijm,Rob W. van der Pluijm,Mallika Imwong,Nguyen Hoang Chau,Nhu Thi Hoa,Nguyen Thuy-Nhien,Ngo Viet Thanh,Podjanee Jittamala,Borimas Hanboonkunupakarn,K Chutasmit,Chalermpon Saelow,Ratchadaporn Runjarern,Weerayuth Kaewmok,Rupam Tripura,Rupam Tripura,Thomas J. Peto,Thomas J. Peto,Sovann Yok,Seila Suon,Sokunthea Sreng,Sivanna Mao,Savuth Oun,Sovannary Yen,Chanaki Amaratunga,Dysoley Lek,Rekol Huy,Mehul Dhorda,Mehul Dhorda,Kesinee Chotivanich,Elizabeth A. Ashley,Elizabeth A. Ashley,Mavuto Mukaka,Mavuto Mukaka,Naomi Waithira,Naomi Waithira,Phaik Yeong Cheah,Phaik Yeong Cheah,Richard J. Maude,Richard J. Maude,Richard J. Maude,Roberto Amato,Richard D. Pearson,Richard D. Pearson,Sónia Gonçalves,Christopher G Jacob,William L Hamilton,Rick M. Fairhurst,Joel Tarning,Joel Tarning,Markus Winterberg,Markus Winterberg,Dominic P. Kwiatkowski,Dominic P. Kwiatkowski,Sasithon Pukrittayakamee,Sasithon Pukrittayakamee,Tran Tinh Hien,Nicholas P. J. Day,Nicholas P. J. Day,Olivo Miotto,Nicholas J. White,Nicholas J. White,Arjen M. Dondorp,Arjen M. Dondorp +62 more
TL;DR: Dihydroartemisinin-piperaquine is not treating malaria effectively across the eastern Greater Mekong subregion, and a highly drug-resistant P falciparum co-lineage is evolving, acquiring new resistance mechanisms, and spreading.
Proceedings ArticleDOI
CNN-based image analysis for malaria diagnosis
Zhaohui Liang,Andrew Powell,Ilker Ersoy,Mahdieh Poostchi,Kamolrat Silamut,Kannappan Palaniappan,Peng Guo,Amir Hossain,Antani Sameer,Richard J. Maude,Jimmy Xiangji Huang,Stefan Jaeger,George R. Thoma +12 more
TL;DR: This study proposes a new and robust machine learning model based on a convolutional neural network (CNN) to automatically classify single cells in thin blood smears on standard microscope slides as either infected or uninfected.