SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images

Mukashyaka, Patience; Sheridan, Todd B.; Foroughi pour, Ali; Chuang, Jeffrey H.

doi:10.1016/j.ebiom.2023.104908

Cited by 5 publications

(1 citation statement)

References 41 publications

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“…This indicator achieved an AUC of 0.962, which is considered a great performance comparable to most machine learning results. 67,68 The optimal cutoff values defined by the point with maximum Youden's index of REA is 23%. 69 At this optimal value, the specificity and sensitivity of REA reach 92.7% and 84.2%, respectively.…”

Section: Recognition Of Carcinoma With Ecm Visualizationmentioning

confidence: 99%

Thickness-Resolved Electrochemiluminescence Microscopy of Extracellular Matrix at Tumor Tissues for Rapid Cancer Diagnosis

Han,

Yang,

Feng

et al. 2024

ACS Appl. Mater. Interfaces

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Section: Recognition Of Carcinoma With Ecm Visualizationmentioning

confidence: 99%

Thickness-Resolved Electrochemiluminescence Microscopy of Extracellular Matrix at Tumor Tissues for Rapid Cancer Diagnosis

Han,

Yang,

Feng

et al. 2024

ACS Appl. Mater. Interfaces

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Multistain Pretraining for Slide Representation Learning in Pathology

Jaume,

Vaidya,

Zhang

et al. 2024

Lecture Notes in Computer Science

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A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification

Wang,

Ni,

Zhou

et al. 2024

J Digit Imaging. Inform. med.

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Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew’s correlation coefficient (MCC), and Cohen’s kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models’ interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen’s kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images.

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SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images

Cited by 5 publications

References 41 publications

Thickness-Resolved Electrochemiluminescence Microscopy of Extracellular Matrix at Tumor Tissues for Rapid Cancer Diagnosis

Thickness-Resolved Electrochemiluminescence Microscopy of Extracellular Matrix at Tumor Tissues for Rapid Cancer Diagnosis

Multistain Pretraining for Slide Representation Learning in Pathology

A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification

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