Search Results for author: Daisuke Komura

Found 4 papers, 1 papers with code

Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis

no code implementations6 Jan 2025 Daisuke Komura, Maki Takao, Mieko Ochi, Takumi Onoyama, Hiroto Katoh, Hiroyuki Abe, Hiroyuki Sano, Teppei Konishi, Toshio Kumasaka, Tomoyuki Yokose, Yohei Miyagi, Tetsuo Ushiku, Shumpei Ishikawa

The tumor microenvironment (TME) plays a crucial role in cancer progression and treatment response, yet current methods for its comprehensive analysis in H&E-stained tissue slides face significant limitations in the diversity of tissue cell types and accuracy.

Decision Making Diversity +4

Multimodal Whole Slide Foundation Model for Pathology

1 code implementation29 Nov 2024 Tong Ding, Sophia J. Wagner, Andrew H. Song, Richard J. Chen, Ming Y. Lu, Andrew Zhang, Anurag J. Vaidya, Guillaume Jaume, Muhammad Shaban, Ahrong Kim, Drew F. K. Williamson, Bowen Chen, Cristina Almagro-Perez, Paul Doucet, Sharifa Sahai, Chengkuan Chen, Daisuke Komura, Akihiro Kawabe, Shumpei Ishikawa, Georg Gerber, Tingying Peng, Long Phi Le, Faisal Mahmood

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL).

Cross-Modal Retrieval model +4

Pathology Foundation Models

no code implementations31 Jul 2024 Mieko Ochi, Daisuke Komura, Shumpei Ishikawa

Pathology has played a crucial role in the diagnosis and evaluation of patient tissue samples obtained from surgeries and biopsies for many years.

Decision Making Prognosis

Machine learning methods for histopathological image analysis

no code implementations4 Sep 2017 Daisuke Komura, Shumpei Ishikawa

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques.

BIG-bench Machine Learning

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