Search Results for author: Jianan Chen

Found 11 papers, 4 papers with code

Cross-Validation Is All You Need: A Statistical Approach To Label Noise Estimation

no code implementations24 Jun 2023 Jianan Chen, Anne Martel

Most existing label noise detection approaches are designed for classification tasks, and data cleaning for outcome prediction analysis is relatively unexplored.

Noise Estimation

Intra-Modal Constraint Loss For Image-Text Retrieval

1 code implementation11 Jul 2022 Jianan Chen, Lu Zhang, Qiong Wang, Cong Bai, Kidiyo Kpalma

Cross-modal retrieval has drawn much attention in both computer vision and natural language processing domains.

Cross-Modal Retrieval Retrieval +1

Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling

no code implementations9 Mar 2022 Jianan Chen, Anne L. Martel

Cancer stage is a large determinant of patient prognosis and management in many cancer types, and is often assessed using medical imaging modalities, such as CT and MRI.

Benchmarking Management +1

AMINN: Autoencoder-based Multiple Instance Neural Network Improves Outcome Prediction of Multifocal Liver Metastases

1 code implementation12 Dec 2020 Jianan Chen, Helen M. C. Cheung, Laurent Milot, Anne L. Martel

Experimental results empirically validated our hypothesis that incorporating imaging features of all lesions improves outcome prediction for multifocal cancer.

How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study

2 code implementations MIDL 2019 Jun Ma, Zhan Wei, Yiwen Zhang, Yixin Wang, Rongfei Lv, Cheng Zhu, Gaoxiang Chen, Jianan Liu, Chao Peng, Lei Wang, Yunpeng Wang, Jianan Chen

The \emph{second contribution} is that we systematically evaluated five benchmark methods on two representative public datasets.

Unsupervised Clustering of Quantitative Imaging Phenotypes using Autoencoder and Gaussian Mixture Model

no code implementations6 Sep 2019 Jianan Chen, Laurent Milot, Helen M. C. Cheung, Anne L. Martel

The performance of the proposed pipeline was evaluated on an institutional MRI cohort of 108 patients with colorectal cancer liver metastases.

Clustering Representation Learning

Multi-layer Domain Adaptation for Deep Convolutional Networks

no code implementations5 Sep 2019 Ozan Ciga, Jianan Chen, Anne Martel

Despite their success in many computer vision tasks, convolutional networks tend to require large amounts of labeled data to achieve generalization.

Domain Adaptation Multi-class Classification

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