hypotension
发表于 2025-3-23 12:54:41
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机构
发表于 2025-3-23 15:18:12
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accomplishment
发表于 2025-3-23 20:16:09
S,R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classificationd is restored by using the learned coefficients to reweight the remaining bands. Two pre-text tasks are designed: (1) S.R-CR, which regresses the linear coefficients, so that the pre-trained model understands the inherent structures of HSIs and the pathological characteristics of different morpholog
厨师
发表于 2025-3-24 00:51:47
Distilling Knowledge from Topological Representations for Pathological Complete Response Predictionrior performance by increasing the accuracy from previously 85.1% to 90.5% in the pCR prediction and reducing the topological computation time by about 66% on a public dataset for breast DCE-MRI images.
世俗
发表于 2025-3-24 02:26:13
SETMIL: Spatial Encoding Transformer-Based Multiple Instance Learning for Pathological Image Analysincoding design in the aggregating module further improves the context-information-encoding ability of SETMIL. (4) SETMIL designs a transformer-based pyramid multi-scale fusion module to comprehensively encode the information with different granularity using multi-scale receptive fields and make the
charisma
发表于 2025-3-24 06:42:01
Clinical-Realistic Annotation for Histopathology Images with Probabilistic Semi-supervision: A Worstkle the challenge, we 1) proposed a different annotation strategy to image data with different levels of disease severity, 2) combined semi- and self-supervised representation learning with probabilistic weakly supervision to make use of the proposed annotations, and 3) illustrated its effectiveness
blackout
发表于 2025-3-24 14:46:17
End-to-End Learning for Image-Based Detection of Molecular Alterations in Digital Pathologycer cases from The Cancer Genome Atlas. Results reach AUC scores of up to 94% and are shown to be competitive with state of the art two-stage pipelines. We believe our approach can facilitate future research in digital pathology and contribute to solve a large range of problems around the prediction
HILAR
发表于 2025-3-24 18:52:16
S5CL: Unifying Fully-Supervised, Self-supervised, and Semi-supervised Learning Through Hierarchical rk on two public histopathological datasets show strong improvements in the case of sparse labels: for a H &E-stained colorectal cancer dataset, the accuracy increases by up to . compared to supervised cross-entropy loss; for a highly imbalanced dataset of single white blood cells from leukemia pati
Left-Atrium
发表于 2025-3-24 21:58:21
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审问
发表于 2025-3-25 01:41:12
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