Prof. Cheng Lu Profile

Prof. Cheng Lu

at Guangdong Provincial People's Hospital

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Publications (2)

Proceedings Article | 13 July 2024 Paper

A multiscale attention network for grading of invasive ductal cancer

Xinxin Zhang, Cheng Lu, Jiayang Bai, Yuli Chen

Proceedings Volume 13208, 132081A (2024) https://doi.org/10.1117/12.3036612

KEYWORDS: Data modeling, Tumor growth modeling, Feature extraction, Tumors, Convolution, Cancer, Transformers, Performance modeling

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Proceedings Article | 7 April 2023 Presentation + Paper

Transformer as a spatially-aware multi-instance learning framework to predict the risk of death for early-stage non-small cell lung cancer

Yufei Zhou, Can Koyuncu, Cristian ‪Barrera, Germán Corredor, Xiangxue Wang, Cheng Lu, Anant Madabhushi

Proceedings Volume 12471, 124710V (2023) https://doi.org/10.1117/12.2654498

KEYWORDS: Transformers, Spatial learning, Lung cancer, Tumors, Medical research, Cancer, Tissues, Feature extraction

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In computational pathology, training and inference of conventional deep convolutional neural networks (CNN) are usually limited to patches of small sizes (e.g., 256 × 256) sampled from whole slide images. In practice, however, diagnostic and prognostic information could lie within the context of tumor microenvironment across multiple regions, far beyond the scope of individual patches. For instance, the spatial relationship of tumor-infiltrating lymphocytes (TIL) across regions of interest might be prognostic for non-small cell lung cancer (NSCLC). This poses a multi-instance learning (MIL) problem, and a single-patch-driven CNN typically fails to learn spatial information and context between multiple patches, especially their spatial relationship. In this work, we present a cell graph-based MIL framework to predict the risk of death for early-stage NSCLC by aggregating feature representation of TIL-enclosing patches according to their spatial relationship. Inspired by PATCHY-SAN, a graph-embedding framework for CNNs, we use graph kernel-based approaches to embed a bag of patches into a sequence with their spatial information encoded into the sequence order. A transformer model was then trained to aggregate patch-level features based on spatial information. We demonstrate the capability of this framework to predict the likelihood of the patient with NSCLC in two cohorts (n=240) to survive for more than 5 years. The training cohort (n=195) comprised hematoxylin and eosin (H&E)-stained whole slide images (WSI), while the testing cohort (n=45) comprised H&E-stained tumor microarrays (TMA). We show that, with the spatial context of multiple patches encoded as an ordered patch sequence, the performance in the testing cohort of our approach achieves an area under the receiver operating characteristic curve (AUC) of 0.836 (p=0.009; HR=5.62), as opposed to a baseline conventional CNN with an AUC of 0.542 (p=0.105; HR=1.66). The results suggest that the Transformer is a generic spatial information aware MIL framework that can learn the spatial relationship of multiple TIL-enclosing patches from the graph representation of immune cells.

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