Cross-Scale Attention Guided Multi-instance Learning for Crohn’s Disease Diagnosis with Pathological Images

Deng, Ruining; Cui, Can; Remedios, Lucas W.; Bao, Shunxing; Womick, R. Michael; Chiron, Sophie; Li, Jia; Roland, Joseph T.; Lau, Ken S.; Liu, Qi; Wilson, Keith T.; Wang, Yaohong; Coburn, Lori A.; Landman, Bennett A.; Huo, Yuankai

doi:10.1007/978-3-031-18814-5_3

Cited by 14 publications

(15 citation statements)

References 29 publications

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“…Although this comparison may seem unfair (comparing SAM's zero-shot performance with fully-trained medical image models), SAM's zero-shot performance has been shown to outperform fully-trained models on nature image datasets. This suggests that SAM has worse zero-shot transfer capability on medical images, which has also been observed in many other studies [9,16,31].…”

Section: Resultssupporting

confidence: 80%

Mo1640: PREDICTION OF THE INCIDENCE OF INFECTIOUS DIARRHEA IN CHINA: A NOVEL VAR PREDICTION MODEL BASED ON BAIDU INDEX

Wu¹,

Zhang²,

Wang³

et al. 2022

Gastroenterology

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Section: Resultssupporting

confidence: 80%

Mo1640: PREDICTION OF THE INCIDENCE OF INFECTIOUS DIARRHEA IN CHINA: A NOVEL VAR PREDICTION MODEL BASED ON BAIDU INDEX

Wu¹,

Zhang²,

Wang³

et al. 2022

Gastroenterology

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“…Deng et al [16] proposed a novel cross-scale MIL algorithm (CS-MIL) that explicitly aggregates inter-scale relationships into a single MIL network for pathology image diagnosis. In addition, the proposed method utilizes cross-scale attention scores to generate importance maps, which enhances the interpretability and comprehensibility of the CS-MIL model.…”

Section: Wsi Exploration Of Idh Gliomas Using a Multiple Instance Lea...mentioning

confidence: 99%

“…Deng et al (2023) [16] TCGA TCGA (n = 613) AUC (0.7737) CS-MIL No Loeffler et al (2022) [17] TCGA TCGA (n = 680) AUC (0.764) DenseNet No Wang et al (2023) [18] TCGA TCGA (n = 940) AUC (86.4) HMT-MIL (Customised architecture) No Faust et al (2022) [19] University of Toronto University of Toronto (n = 47) Accuracy (99.3%) VGG19 No Fang et al (2023) [20] TCGA Xiangya Hospital TCGA (n = 844) Xiangya (n = 116) AUC (0.827 ± 0.0465) Multi-Beholder (Customised architecture)…”

Section: Nomentioning

confidence: 99%

Insight into deep learning for glioma IDH medical image analysis: A systematic review

Lv,

Liu,

Sun

et al. 2024

Medicine

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Background: Deep learning techniques explain the enormous potential of medical image analysis, particularly in digital pathology. Concurrently, molecular markers have gained increasing significance over the past decade in the context of glioma patients, providing novel insights into diagnosis and more personalized treatment options. Deep learning combined with imaging and molecular analysis enables more accurate prognostication of patients, more accurate treatment plan proposals, and accurate biomarker (IDH) prediction for gliomas. This systematic study examines the development of deep learning techniques for IDH prediction using histopathology images, spanning the period from 2019 to 2023. Method: The study adhered to the PRISMA reporting requirements, and databases including PubMed, Google Scholar, Google Search, and preprint repositories (such as arXiv) were systematically queried for pertinent literature spanning the period from 2019 to the 30th of 2023. Search phrases related to deep learning, digital pathology, glioma, and IDH were collaboratively utilized. Results: Fifteen papers meeting the inclusion criteria were included in the analysis. These criteria specifically encompassed studies utilizing deep learning for the analysis of hematoxylin and eosin images to determine the IDH status in patients with gliomas. Conclusions: When predicting the status of IDH, the classifier built on digital pathological images demonstrates exceptional performance. The study’s predictive effectiveness is enhanced with the utilization of the appropriate deep learning model. However, external verification is necessary to showcase their resilience and universality. Larger sample sizes and multicenter samples are necessary for more comprehensive research to evaluate performance and confirm clinical advantages.

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“…To achieve this, the SAM [53] is created. SAM leverages prompt engineering to tackle general downstream segmentation tasks by utilizing the prompt segmentation task as a pre-training objective [97]. To enhance the model's flexibility in adapting to prompts and to improve its robustness against interference, SAM is divided into three components: the image encoder, the prompt encoder, and the mask decoder.…”

Section: Foundation Modelsmentioning

confidence: 99%

School Mathematics Textbooks in China

Wang¹

2015

East China Normal University Scientific Reports

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Let µ and ν be two probability measures on R d , where µ(dx) = e −V (x) dx R d e −V (x) dx for some V ∈ C 1 (R d ). Explicit sufficient conditions on V and ν are presented such that µ * ν satisfies the log-Sobolev, Poincaré and super Poincaré inequalities. In particular, if V (x) = λ|x| 2 for some λ > 0 and ν(e λθ|•| 2 ) < ∞ for some θ > 1, then µ * ν satisfies the log-Sobolev inequality. This improves and extends the recent results on the log-Sobolev inequality derived in [20] for convolutions of the Gaussian measure and compactly supported probability measures. On the other hand, it is well known that the log-Sobolev inequality for µ * ν implies ν(e ε|•| 2 ) < ∞ for some ε > 0. RésuméDes conditions explicites suffisantes sur V et ν sont présentées telles que µ * ν satisfait des inégalités de Sobolev logarithmique, de Poincaré et de super-Poincaré. En particulier, si V (x) = λ|x| 2 pour quelque λ > 0 et ν(e λθ|•| 2 ) < ∞ avec θ > 1, alors µ * ν satisfait l'inégalité de Sobolev logarithmique. Cela améliore et étend des résultats récents sur l'inégalité de Sobolev logarithmique obtenus dans [20] pour des convolutions de la mesure de Gauss et des mesures de probabilité à support compact. D'autre part, il est bien connu que l'inégalité de Sobolev logarithmique pour µ * ν implique ν(e ε|•| 2 ) < ∞ pour quelque ε > 0.

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Cross-Scale Attention Guided Multi-instance Learning for Crohn’s Disease Diagnosis with Pathological Images

Cited by 14 publications

References 29 publications

Mo1640: PREDICTION OF THE INCIDENCE OF INFECTIOUS DIARRHEA IN CHINA: A NOVEL VAR PREDICTION MODEL BASED ON BAIDU INDEX

Mo1640: PREDICTION OF THE INCIDENCE OF INFECTIOUS DIARRHEA IN CHINA: A NOVEL VAR PREDICTION MODEL BASED ON BAIDU INDEX

Insight into deep learning for glioma IDH medical image analysis: A systematic review

School Mathematics Textbooks in China

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