Výsledky vyhledávání - "Zhang, Bodong"

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SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images

Autor: Manoochehri, Hamid, Zhang, Bodong, Knudsen, Beatrice S., Tasdizen, Tolga

Self-supervised learning has become a cornerstone in various areas, particularly histopathological image analysis. Image augmentation plays a crucial role in self-supervised learning, as it generates variations in image samples. However, traditional

Externí odkaz: http://arxiv.org/abs/2410.17514

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DuoFormer: Leveraging Hierarchical Visual Representations by Local and Global Attention

Autor: Tang, Xiaoya, Zhang, Bodong, Knudsen, Beatrice S., Tasdizen, Tolga

We here propose a novel hierarchical transformer model that adeptly integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the advanced representational potential of Vision Transformers (ViTs). Addressing the lack

Externí odkaz: http://arxiv.org/abs/2407.13920

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CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification

Autor: Zhang, Bodong, Manoochehri, Hamid, Ho, Man Minh, Fooladgar, Fahimeh, Chong, Yosep, Knudsen, Beatrice S., Sirohi, Deepika, Tasdizen, Tolga

Histopathological image classification is an important task in medical image analysis. Recent approaches generally rely on weakly supervised learning due to the ease of acquiring case-level labels from pathology reports. However, patch-level classifi

Externí odkaz: http://arxiv.org/abs/2312.06978

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Stain Based Contrastive Co-training for Histopathological Image Analysis

Autor: Zhang, Bodong, Knudsen, Beatrice, Sirohi, Deepika, Ferrero, Alessandro, Tasdizen, Tolga

We propose a novel semi-supervised learning approach for classification of histopathology images. We employ strong supervision with patch-level annotations combined with a novel co-training loss to create a semi-supervised learning framework. Co-trai

Externí odkaz: http://arxiv.org/abs/2206.12505

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