TY - GEN
T1 - MM-DCNet
T2 - 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024
AU - Ma, Gege
AU - Jin, Yuan
AU - Lyu, Tianling
AU - Chen, Geng
AU - Wu, Zhuoxuan
AU - Zhao, Jiaqi
AU - Zhu, Wentao
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Early and accurate subtypes classifications for lung cancer patients is critical for the following-up treatments and prognosis. CT scan, being the non-invasive and fast-imaging modality, is one of the most common used techniques for cancer diagnosis, thus, the CT-based automatic analysis systems are in high demand. However, the accuracy of such models are limited due to the relative low resolution of CT images. Clinically, pathological examination is regarded as "gold standard"in cancer diagnosis, so the introduction of such cellular-level information into CT-based model is expected to improve model's accuracy. However, the invasive pathological examination may not be applicable to all clinical scenarios, leading to the presence of numerous unbalanced multi-modality images, i.e., paired CT/pathology images as well as standalone CT images. In this work, we propose a novel classification model, i.e., multi-scale and multi-modality dynamic convolutional network (MM-DCNet), to assist the diagnosis of lung cancer subtypes using unbalanced CT and pathological images. Within the model, we designed a dynamic convolutional module that empowers the model to adaptively adjust its parameters according to different inputs, e.g., the paired multi-modality images and the single-modality image, and consequently exploit the value of all clinical datasets. Furthermore, we designed a contrastive learning module to acquire the cross-modality correlations from paired CT/pathological images and subsequently leverage such correlations as priors to lead the model to more accurate predictions even in the absence of pathology. Experiment results have demonstrated the superiority of our proposed model in lung cancer subtypes diagnosis.
AB - Early and accurate subtypes classifications for lung cancer patients is critical for the following-up treatments and prognosis. CT scan, being the non-invasive and fast-imaging modality, is one of the most common used techniques for cancer diagnosis, thus, the CT-based automatic analysis systems are in high demand. However, the accuracy of such models are limited due to the relative low resolution of CT images. Clinically, pathological examination is regarded as "gold standard"in cancer diagnosis, so the introduction of such cellular-level information into CT-based model is expected to improve model's accuracy. However, the invasive pathological examination may not be applicable to all clinical scenarios, leading to the presence of numerous unbalanced multi-modality images, i.e., paired CT/pathology images as well as standalone CT images. In this work, we propose a novel classification model, i.e., multi-scale and multi-modality dynamic convolutional network (MM-DCNet), to assist the diagnosis of lung cancer subtypes using unbalanced CT and pathological images. Within the model, we designed a dynamic convolutional module that empowers the model to adaptively adjust its parameters according to different inputs, e.g., the paired multi-modality images and the single-modality image, and consequently exploit the value of all clinical datasets. Furthermore, we designed a contrastive learning module to acquire the cross-modality correlations from paired CT/pathological images and subsequently leverage such correlations as priors to lead the model to more accurate predictions even in the absence of pathology. Experiment results have demonstrated the superiority of our proposed model in lung cancer subtypes diagnosis.
KW - CT
KW - Image classification
KW - multi-modality
KW - neural network
KW - unbalanced dataset
UR - http://www.scopus.com/inward/record.url?scp=85203305784&partnerID=8YFLogxK
U2 - 10.1109/ISBI56570.2024.10635717
DO - 10.1109/ISBI56570.2024.10635717
M3 - 会议稿件
AN - SCOPUS:85203305784
T3 - Proceedings - International Symposium on Biomedical Imaging
BT - IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings
PB - IEEE Computer Society
Y2 - 27 May 2024 through 30 May 2024
ER -