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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 9 of 9  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
MI 2024-03-03
09:05
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
[Short Paper] Generation of Counterfactual Pathology Images of Malignant Lymphoma using Diffusion Models
Ryoichi Koga, Tatsuya Yokota (NIT), Kouichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi (Kurume Univ.), Noriaki Hashimoto (RIKEN), Ichiro Takeuchi (Nagoya Univ.), Hidekata Hontani (NIT) MI2023-30
Malignant lymphoma has more than 70 subtypes. In the pathological diagnosis, a pathological image is observed to identif... [more] MI2023-30
pp.1-2
MI 2024-03-03
16:42
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
[Short Paper] Using Label Uncertainty for Learning Cell Nuclei Type Classifier with Strongly Noisy Supervised Signals
Shingo Koide, Mauricio Kugler, Tatsuya Yokota (NIT), Koichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi (Kurume Univ.), Noriaki Hashimoto (RIKEN), Ichiro Takeuchi (Nagoya Univ.), Hidekata Hontani (NIT) MI2023-57
In this study, we construct a type classifier for cell nuclei of malignant lymphomas. Labelling by type is not easy, eve... [more] MI2023-57
pp.79-80
MI 2024-03-04
11:10
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
[Short Paper] Identification of follicle segmentation and subtype in a lymph node HE-stained image based on the set of cell nuclei
Mizuki Moribe, Tatsuya Yokota (NIT), Koichi Oshima, Hiroaki Miyoshi, Miharu Nagaishi (Kurume Univ.), Noriaki Hashimoto (RIKEN), Ichiro Takeuchi (NU), Hidekata Hontani (NIT) MI2023-72
In this paper, we report on a method for follicle segmentation and the identification of malignant lymphoma subtypes usi... [more] MI2023-72
pp.131-132
MI 2023-09-08
10:05
Osaka
(Primary: On-site, Secondary: Online)
[Short Paper] Construction of Cell Nucleus Classifier using Complementary-Label Learning towards the Quantification of Grading for Follicular Lymphoma
Ryoichi Koga, Mauricio Kugler, Tatsuya Yokota (NIT), Kouichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi (Kurume Univ.), Noriaki Hashimoto (RIKEN), Ichiro Takeuchi (Nagoya Univ.), Hidekata Hontani (NIT) MI2023-14
In this paper, we report the cell type classification from a pathological image toward the subtype classification of mal... [more] MI2023-14
pp.1-2
MI 2023-03-06
17:56
Okinawa OKINAWA SEINENKAIKAN
(Primary: On-site, Secondary: Online)
[Short Paper] Generation of Counterfactual Images towards the Construction of Quantitatively Criteria in Malignant Lymphoma
Ryoichi Koga, Mauricio Kugler, Tatsuya Yokota (NIT), Kouichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi (KU), Noriaki Hashimoto, Ichiro Takeuchi (NU), Hidekata Hontani (NIT) MI2022-100
In pathological diagnosis of malignant lymphoma, a H&E-staind pathological image is observed to identify the subtype. Ho... [more] MI2022-100
pp.123-124
MI 2022-07-08
14:00
Hokkaido
(Primary: On-site, Secondary: Online)
Cell type-specific tumor degree estimation in malignant lymphoma pathology images
Hiroki Masuda (NITech), Noriaki Hashimoto (RIKEN), Yusuke Takagi (NITech), Hiroyuki Hanada (RIKEN), Hiroaki Miyoshi, Kensaku Sato, Koichi Oshima (Kurume Univ.), Hidekata Hontani (NITech), Ichiro Takeuchi (Nagoya Univ./RIKEN) MI2022-32
In the pathological diagnosis flow of malignant lymphoma, a type of blood cancer, it is important to identify the type o... [more] MI2022-32
pp.1-6
MI 2022-01-27
13:54
Online Online [Short Paper] Case-based Similar Image Retrieval for Pathological Images of Malignant Lymphoma Using Deep Metric Learning
Noriaki Hashimoto (RIKEN), Yusuke Takagi, Hiroki Masuda (NITech), Hiroaki Miyoshi, Kei Kohno, Miharu Nagaishi, Kensaku Sato, Koichi Ohshima (Kurume Univ.), Hidekata Hontani (NITech), Ichiro Takeuchi (NITech/RIKEN) MI2021-78
We propose a novel method of case-based similar image retrieval for histopathological images of malignant lymphoma. We e... [more] MI2021-78
pp.144-145
MI 2021-07-09
11:00
Online Online [Short Paper] Construction of Subtype Classifier for Malignant Lymphoma based on H&E-stained Images using Immuno-stainning Data
Yuki Hirono (NIT), Noriaki Hashimoto (RIKEN), Kugler Mauricio, Tatsuya Yokota (NIT), Miharu Nagaishi (Kurume Univ.), Hiroaki Miyoshi, Koichi Oshima (Kurume Univ./JSP), Ichiro Takeuchi (NIT/RIKEN), Hidekata Hontani (NIT) MI2021-16
In pathological diagnosis of malignant lymphoma, a HE image is observed at first and then a set of immunostained images ... [more] MI2021-16
pp.31-32
PRMU, MI, IPSJ-CVIM [detail] 2019-09-04
16:20
Okayama   Domain-adversarial multiple instance learning for subtype classification of malignant lymphoma
Daisuke Fukushima, Ryoichi Koga, Noriaki Hashimoto, Kaho Ko (Nagoya Inst. of Tech), Masato Nakaguro, Kei Kohno, Shigeo Nakamura (Nagoya Univ. Hospital), Hidekata Hontani (Nagoya Inst of Tech), Ichiro Takeuchi (Nagoya Inst. of Tech/RIKEN/NIMS) PRMU2019-15 MI2019-34
We classify subtypes of malignant lymphoma using convolutional neural network with digital pathological images as input ... [more] PRMU2019-15 MI2019-34
pp.19-24
 Results 1 - 9 of 9  /   
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