A deep learning framework deploying segment anything to detect pan-cancer mitotic figures from haematoxylin and eosin-stained slides.

Autor: Shen Z; Department of Medical Physics and Biomedical Engineering, University College London, London, UK. zhuoyan.shen.18@ucl.ac.uk., Simard M; Department of Medical Physics and Biomedical Engineering, University College London, London, UK., Brand D; Department of Medical Physics and Biomedical Engineering, University College London, London, UK.; Department of Radiotherapy, University College London Hospitals NHS Foundation Trust, London, UK., Andrei V; Research Department of Pathology, University College London Cancer Institute, London, UK.; Cellular and Molecular Pathology, Royal National Orthopaedic Hospital NHS Foundation Trust, Middlesex, UK., Al-Khader A; Research Department of Pathology, University College London Cancer Institute, London, UK.; Cellular and Molecular Pathology, Royal National Orthopaedic Hospital NHS Foundation Trust, Middlesex, UK., Oumlil F; Cellular and Molecular Pathology, Royal National Orthopaedic Hospital NHS Foundation Trust, Middlesex, UK., Trevers K; Research Department of Pathology, University College London Cancer Institute, London, UK.; Cellular and Molecular Pathology, Royal National Orthopaedic Hospital NHS Foundation Trust, Middlesex, UK., Butters T; Research Department of Pathology, University College London Cancer Institute, London, UK., Haefliger S; Research Department of Pathology, University College London Cancer Institute, London, UK.; Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, CH, Switzerland., Kara E; Department of Neurology, Rutgers Biomedical and Health Sciences, Rutgers University, NJ, USA., Amary F; Research Department of Pathology, University College London Cancer Institute, London, UK.; Cellular and Molecular Pathology, Royal National Orthopaedic Hospital NHS Foundation Trust, Middlesex, UK., Tirabosco R; Research Department of Pathology, University College London Cancer Institute, London, UK.; Cellular and Molecular Pathology, Royal National Orthopaedic Hospital NHS Foundation Trust, Middlesex, UK., Cool P; Department of Orthopaedics, The Robert Jones and Agnes Hunt Orthopaedic Hospital, Oswestry, UK.; School of Medicine, Keele University, Newcastle, UK., Royle G; Department of Medical Physics and Biomedical Engineering, University College London, London, UK., Hawkins MA; Department of Medical Physics and Biomedical Engineering, University College London, London, UK.; Department of Radiotherapy, University College London Hospitals NHS Foundation Trust, London, UK., Flanagan AM; Research Department of Pathology, University College London Cancer Institute, London, UK.; Cellular and Molecular Pathology, Royal National Orthopaedic Hospital NHS Foundation Trust, Middlesex, UK., Collins-Fekete CA; Department of Medical Physics and Biomedical Engineering, University College London, London, UK.

Publikováno v: Communications biology [Commun Biol] 2024 Dec 19; Vol. 7 (1), pp. 1674. Date of Electronic Publication: 2024 Dec 19.

Weakly supervised deep learning image analysis can differentiate melanoma from naevi on haematoxylin and eosin-stained histopathology slides.

Autor: Maher NG; Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.; Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.; Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia., Danaei Mehr H; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia., Cong C; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia., Adegoke NA; Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.; Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia., Vergara IA; Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.; Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia., Liu S; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia., Scolyer RA; Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.; Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.; Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.

Publikováno v: Journal of the European Academy of Dermatology and Venereology : JEADV [J Eur Acad Dermatol Venereol] 2024 Dec; Vol. 38 (12), pp. 2250-2258. Date of Electronic Publication: 2024 Aug 31.

Quantifying the recovery process of skeletal muscle on hematoxylin and eosin stained images via learning from label proportion.

Autor: Yamaoka Y; Graduate School of Information Science and Technology, Osaka University, Osaka, 565-0871, Japan., Chan WI; Graduate School of Information Science and Technology, Osaka University, Osaka, 565-0871, Japan., Seno S; Graduate School of Information Science and Technology, Osaka University, Osaka, 565-0871, Japan. senoo@ist.osaka-u.ac.jp., Iwamori K; Graduate School of Pharmaceutical Sciences, Osaka University, Osaka, 565-0871, Japan., Fukada SI; Graduate School of Pharmaceutical Sciences, Osaka University, Osaka, 565-0871, Japan., Matsuda H; Graduate School of Information Science and Technology, Osaka University, Osaka, 565-0871, Japan.

Publikováno v: Scientific reports [Sci Rep] 2024 Nov 07; Vol. 14 (1), pp. 27044. Date of Electronic Publication: 2024 Nov 07.

An Immunofluorescence-Guided Segmentation Model in Hematoxylin and Eosin Images Is Enabled by Tissue Artifact Correction Using a Cycle-Consistent Generative Adversarial Network.

Autor: Wiedenmann M; Department of Computer and Information Science, University of Konstanz, Konstanz, Germany., Barch M; Department of Research Pathology, Genentech Inc, South San Francisco, California., Chang PS; Department of Research Pathology, Genentech Inc, South San Francisco, California., Giltnane J; Department of Research Pathology, Genentech Inc, South San Francisco, California., Risom T; Department of Research Pathology, Genentech Inc, South San Francisco, California. Electronic address: risomt@gene.com., Zijlstra A; Department of Research Pathology, Genentech Inc, South San Francisco, California; Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee.

Publikováno v: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc [Mod Pathol] 2024 Nov; Vol. 37 (11), pp. 100591. Date of Electronic Publication: 2024 Aug 13.

Analysis of Two Oncological Drugs Futibatinib and Capivasertib via Ion-Pairing With Eosin Y as a Spectrofluorimetric and Spectrophotometric Probe.

Autor: Salem H; Pharmaceutical Chemistry Department, Faculty of Pharmacy, Deraya University, New Minia, Egypt., Emad N; Pharmaceutical Chemistry Department, Faculty of Pharmacy, Deraya University, New Minia, Egypt., El-Araby M; Pharmaceutical Chemistry Department, Faculty of Pharmacy, Deraya University, New Minia, Egypt., Samir B; Pharmaceutical Chemistry Department, Faculty of Pharmacy, Deraya University, New Minia, Egypt., Abdelaziz A; Pharmaceutical Chemistry Department, Faculty of Pharmacy, Deraya University, New Minia, Egypt.

Publikováno v: Luminescence : the journal of biological and chemical luminescence [Luminescence] 2024 Oct; Vol. 39 (10), pp. e4919.