ResNet50 | configuration

Overview

Published histology patch encoder whose frozen features the HEST authors scored with a Random Forest regression head.

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Evaluations and results

10 evaluations · 10 results. Different protocols are not a single leaderboard.

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: ResNet50	Task: HEST-Benchmark CCRCC: Gene expression prediction from histology, Clear cell renal cell carcinoma Dataset subset: HEST-Benchmark CCRCC (HEST-Benchmark split)	0.136 ±0.04 pearson_r correlation · higher Uncertainty: type: standard_deviation; value: 0.04 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ResNet50 on HEST-Benchmark CCRCC: Gene expression prediction from histology, Clear cell renal cell carcinoma Random Forest regression with 70 trees over frozen patch features, averaged over folds or patients. Aggregation: Not reported HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis · Table 1, row(CCRCC), column(ResNet50)
Configuration: ResNet50	Task: HEST-Benchmark COAD: Gene expression prediction from histology, Colon adenocarcinoma Dataset subset: HEST-Benchmark COAD (HEST-Benchmark split)	0.107 ±0.06 pearson_r correlation · higher Uncertainty: type: standard_deviation; value: 0.06 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ResNet50 on HEST-Benchmark COAD: Gene expression prediction from histology, Colon adenocarcinoma Random Forest regression with 70 trees over frozen patch features, averaged over folds or patients. Aggregation: Not reported HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis · Table 1, row(COAD), column(ResNet50)
Configuration: ResNet50	Task: HEST-Benchmark HCC: Gene expression prediction from histology, Hepatocellular carcinoma Dataset subset: HEST-Benchmark HCC (HEST-Benchmark split)	0.034 ±0.01 pearson_r correlation · higher Uncertainty: type: standard_deviation; value: 0.01 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ResNet50 on HEST-Benchmark HCC: Gene expression prediction from histology, Hepatocellular carcinoma Random Forest regression with 70 trees over frozen patch features, averaged over folds or patients. Aggregation: Not reported HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis · Table 1, row(HCC), column(ResNet50)
Configuration: ResNet50	Task: HEST-Benchmark IDC: Gene expression prediction from histology, Invasive ductal carcinoma Dataset subset: HEST-Benchmark IDC (HEST-Benchmark split)	0.440 ±0.03 pearson_r correlation · higher Uncertainty: type: standard_deviation; value: 0.03 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ResNet50 on HEST-Benchmark IDC: Gene expression prediction from histology, Invasive ductal carcinoma Random Forest regression with 70 trees over frozen patch features, averaged over folds or patients. Aggregation: Not reported HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis · Table 1, row(IDC), column(ResNet50)
Configuration: ResNet50	Task: HEST-Benchmark LUNG: Gene expression prediction from histology, Lung Dataset subset: HEST-Benchmark LUNG (HEST-Benchmark split)	0.497 ±0.01 pearson_r correlation · higher Uncertainty: type: standard_deviation; value: 0.01 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ResNet50 on HEST-Benchmark LUNG: Gene expression prediction from histology, Lung Random Forest regression with 70 trees over frozen patch features, averaged over folds or patients. Aggregation: Not reported HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis · Table 1, row(LUNG), column(ResNet50)
Configuration: ResNet50	Task: HEST-Benchmark LYMPH_IDC: Gene expression prediction from histology, Lymph node metastasis of invasive ductal carcinoma Dataset subset: HEST-Benchmark LYMPH_IDC (HEST-Benchmark split)	0.205 ±0.05 pearson_r correlation · higher Uncertainty: type: standard_deviation; value: 0.05 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ResNet50 on HEST-Benchmark LYMPH_IDC: Gene expression prediction from histology, Lymph node metastasis of invasive ductal carcinoma Random Forest regression with 70 trees over frozen patch features, averaged over folds or patients. Aggregation: Not reported HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis · Table 1, row(LYMPH_IDC), column(ResNet50)
Configuration: ResNet50	Task: HEST-Benchmark PAAD: Gene expression prediction from histology, Pancreatic adenocarcinoma Dataset subset: HEST-Benchmark PAAD (HEST-Benchmark split)	0.389 ±0.07 pearson_r correlation · higher Uncertainty: type: standard_deviation; value: 0.07 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ResNet50 on HEST-Benchmark PAAD: Gene expression prediction from histology, Pancreatic adenocarcinoma Random Forest regression with 70 trees over frozen patch features, averaged over folds or patients. Aggregation: Not reported HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis · Table 1, row(PAAD), column(ResNet50)
Configuration: ResNet50	Task: HEST-Benchmark PRAD: Gene expression prediction from histology, Prostate adenocarcinoma Dataset subset: HEST-Benchmark PRAD (HEST-Benchmark split)	0.318 ±0.02 pearson_r correlation · higher Uncertainty: type: standard_deviation; value: 0.02 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ResNet50 on HEST-Benchmark PRAD: Gene expression prediction from histology, Prostate adenocarcinoma Random Forest regression with 70 trees over frozen patch features, averaged over folds or patients. Aggregation: Not reported HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis · Table 1, row(PRAD), column(ResNet50)
Configuration: ResNet50	Task: HEST-Benchmark READ: Gene expression prediction from histology, Rectum adenocarcinoma Dataset subset: HEST-Benchmark READ (HEST-Benchmark split)	0.051 ±0.08 pearson_r correlation · higher Uncertainty: type: standard_deviation; value: 0.08 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ResNet50 on HEST-Benchmark READ: Gene expression prediction from histology, Rectum adenocarcinoma Random Forest regression with 70 trees over frozen patch features, averaged over folds or patients. Aggregation: Not reported HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis · Table 1, row(READ), column(ResNet50)
Configuration: ResNet50	Task: HEST-Benchmark SKCM: Gene expression prediction from histology, Skin cutaneous melanoma Dataset subset: HEST-Benchmark SKCM (HEST-Benchmark split)	0.446 ±0.06 pearson_r correlation · higher Uncertainty: type: standard_deviation; value: 0.06 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ResNet50 on HEST-Benchmark SKCM: Gene expression prediction from histology, Skin cutaneous melanoma Random Forest regression with 70 trees over frozen patch features, averaged over folds or patients. Aggregation: Not reported HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis · Table 1, row(SKCM), column(ResNet50)

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HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis · Original source · Version pinned by URL and artifact SHA256 when available

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Technical metadata and extraction receipts

Stable ID: hest-method-resnet50

areas: cells-tissues
source locator: Table 1, column(ResNet50)
missing metadata: checkpoint revision: unreported; parameters: unextracted

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