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Academic Journal

A deep learning-based multimodal medical imaging model for breast cancer screening.

Authors : Chen J; School of Automation, Central South University, Changsha, Hunan, 410083, China.; Xiangjiang Laboratory, Changsha, 410205, China.

Subjects: Deep Learning* ; Breast Neoplasms*/Breast Neoplasms*/Breast Neoplasms*/diagnostic imaging ; Breast Neoplasms*/Breast Neoplasms*/Breast Neoplasms*/diagnosis

Source: Scientific reports [Sci Rep] 2025 Apr 26; Vol. 15 (1), pp. 14696. Date of Electronic Publication: 2025 Apr 26.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

Enhancing efficient deep learning models with multimodal, multi-teacher insights for medical image segmentation.

Authors : Hossain KF; Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, 89557, USA. .; Kamran SA

Subjects: Deep Learning* ; Image Processing, Computer-Assisted*/Image Processing, Computer-Assisted*/Image Processing, Computer-Assisted*/methods; Humans

Source: Scientific reports [Sci Rep] 2025 May 07; Vol. 15 (1), pp. 15948. Date of Electronic Publication: 2025 May 07.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI.

Authors : Seshimo H; Graduate School of Information Science, University of Hyogo, Kobe 650-0047, Japan.; Rashed EA

Subjects: Brain Neoplasms*/Brain Neoplasms*/Brain Neoplasms*/diagnostic imaging ; Brain Neoplasms*/Brain Neoplasms*/Brain Neoplasms*/pathology ; Magnetic Resonance Imaging*/Magnetic Resonance Imaging*/Magnetic Resonance Imaging*/methods

Source: Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Nov 27; Vol. 24 (23). Date of Electronic Publication: 2024 Nov 27.Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220

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Academic Journal

Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.

Authors : Krasniqi E; Phase IV Clinical Studies Unit, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy. .; Filomeno L

Subjects: Deep Learning* ; Breast Neoplasms*/Breast Neoplasms*/Breast Neoplasms*/therapy ; Neoadjuvant Therapy*/Neoadjuvant Therapy*/Neoadjuvant Therapy*/methods

Source: Biology direct [Biol Direct] 2025 Jun 23; Vol. 20 (1), pp. 72. Date of Electronic Publication: 2025 Jun 23.Publisher: BioMed Central Country of Publication: England NLM ID: 101258412 Publication Model: Electronic Cited Medium: Internet ISSN: 1745-6150

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Academic Journal

Multimodal CustOmics: A unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology.

Authors : Benkirane H; Université Paris-Saclay, CentraleSupélec, Laboratory of Mathematics and Computer Science (MICS), Gif-sur-Yvette, France.; IHU PRISM, National PRecISion Medicine Center in Oncology, Gustave Roussy, Villejuif, France.

Subjects: Deep Learning* ; Neoplasms*/Neoplasms*/Neoplasms*/genetics ; Neoplasms*/Neoplasms*/Neoplasms*/pathology

Source: PLoS computational biology [PLoS Comput Biol] 2025 Jun 17; Vol. 21 (6), pp. e1013012. Date of Electronic Publication: 2025 Jun 17 (Print Publication: 2025).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet

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Academic Journal

Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study.

Authors : Ma Q; Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.

Subjects: Rectal Neoplasms*/Rectal Neoplasms*/Rectal Neoplasms*/diagnostic imaging ; Rectal Neoplasms*/Rectal Neoplasms*/Rectal Neoplasms*/pathology ; Rectal Neoplasms*/Rectal Neoplasms*/Rectal Neoplasms*/diagnosis

Source: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2025 Jun 05; Vol. 25 (1), pp. 209. Date of Electronic Publication: 2025 Jun 05.Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947

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Academic Journal

Student engagement assessment using multimodal deep learning.

Authors : Yan L; College of Mathematics and Statistics, Huanggang Normal University, Huanggang, Hubei, China.; Wu X

Subjects: Deep Learning* ; Students*/Students*/Students*/psychology ; Educational Measurement*/Educational Measurement*/Educational Measurement*/methods

Source: PloS one [PLoS One] 2025 Jun 10; Vol. 20 (6), pp. e0325377. Date of Electronic Publication: 2025 Jun 10 (Print Publication: 2025).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Academic Journal

Multimodal deep learning for chemical toxicity prediction and management.

Authors : Hong J; ROK Army Signal School, Daejeon, 34059, South Korea.; Kwon H

Subjects: Deep Learning* ; Toxicity Tests*

Source: Scientific reports [Sci Rep] 2025 Jun 03; Vol. 15 (1), pp. 19491. Date of Electronic Publication: 2025 Jun 03.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease.

Authors : Qin X; Department of Ultrasound, Chengdu Second People's Hospital, Chengdu, Sichuan Province, China.; Liu X

Subjects: Deep Learning* ; Renal Insufficiency, Chronic*/Renal Insufficiency, Chronic*/Renal Insufficiency, Chronic*/complications ; Renal Insufficiency, Chronic*/Renal Insufficiency, Chronic*/Renal Insufficiency, Chronic*/diagnostic imaging

Source: Renal failure [Ren Fail] 2024 Dec; Vol. 46 (2), pp. 2417740. Date of Electronic Publication: 2024 Oct 22.Publisher: Informa Healthcare Country of Publication: England NLM ID: 8701128 Publication Model: Print-Electronic Cited Medium: Internet ISSN:

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Academic Journal

Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.

Authors : Sasagasako T; Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.; Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, Kyoto, Japan.

Subjects: Glioblastoma*/Glioblastoma*/Glioblastoma*/diagnostic imaging ; Glioblastoma*/Glioblastoma*/Glioblastoma*/surgery ; Glioblastoma*/Glioblastoma*/Glioblastoma*/pathology

Source: PloS one [PLoS One] 2024 Nov 11; Vol. 19 (11), pp. e0303002. Date of Electronic Publication: 2024 Nov 11 (Print Publication: 2024).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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A deep learning-based multimodal medical imaging model for breast cancer screening.

Enhancing efficient deep learning models with multimodal, multi-teacher insights for medical image segmentation.

Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI.

Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.

Multimodal CustOmics: A unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology.

Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study.

Student engagement assessment using multimodal deep learning.

Multimodal deep learning for chemical toxicity prediction and management.

Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease.

Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.

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