Automated HER2 scoring with uncertainty quantification using lensfree holography and deep learning

• URL: http://arxiv.org/abs/2601.18219v1
• Date: Mon, 26 Jan 2026 07:09:08 GMT
• Title: Automated HER2 scoring with uncertainty quantification using lensfree holography and deep learning
• Authors: Che-Yung Shen, Xilin Yang, Yuzhu Li, Leon Lenk, Aydogan Ozcan,
• Abstract summary: We present a compact and cost-effective lensfree holography platform integrated with deep learning for automated HER2 scoring.<n>The system captures lensfree diffraction patterns of stained HER2 tissue sections under RGB laser illumination.<n>It acquires complex field information over a sample area of 1,250 mm2 at an effective throughput of 84 mm2 per minute.
• Score: 0.815557531820863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate assessment of human epidermal growth factor receptor 2 (HER2) expression is critical for breast cancer diagnosis, prognosis, and therapy selection; yet, most existing digital HER2 scoring methods rely on bulky and expensive optical systems. Here, we present a compact and cost-effective lensfree holography platform integrated with deep learning for automated HER2 scoring of immunohistochemically stained breast tissue sections. The system captures lensfree diffraction patterns of stained HER2 tissue sections under RGB laser illumination and acquires complex field information over a sample area of ~1,250 mm^2 at an effective throughput of ~84 mm^2 per minute. To enhance diagnostic reliability, we incorporated an uncertainty quantification strategy based on Bayesian Monte Carlo dropout, which provides autonomous uncertainty estimates for each prediction and supports reliable, robust HER2 scoring, with an overall correction rate of 30.4%. Using a blinded test set of 412 unique tissue samples, our approach achieved a testing accuracy of 84.9% for 4-class (0, 1+, 2+, 3+) HER2 classification and 94.8% for binary (0/1+ vs. 2+/3+) HER2 scoring with uncertainty quantification. Overall, this lensfree holography approach provides a practical pathway toward portable, high-throughput, and cost-effective HER2 scoring, particularly suited for resource-limited settings, where traditional digital pathology infrastructure is unavailable.

Related papers

• DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights [54.87947751720332]
  Accurate brain tumor segmentation is significant for clinical diagnosis and treatment.<n>Mamba-based State Space Models have demonstrated promising performance.<n>We propose a dual-resolution bi-directional Mamba that captures multi-scale long-range dependencies with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-10-16T07:31:21Z)
• BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images [0.0]
  "BlurryScope" is a cost-effective and compact solution for automated inspection and analysis of tissue sections.<n>We implemented automated classification of human epidermal growth factor receptor 2 (HER2) scores on motion-red images of stained breast tissue sections.<n>We achieved testing accuracies of 79.3% and 89.7% for 4-class (0, 1+, 2+, 3+) and 2-class (0/1+, 2+/3+) HER2 classification, respectively.
  arXiv  Detail & Related papers  (2024-10-23T04:46:36Z)
• Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling [3.711848341917877]
  We introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in IHC-stained BC tissue images. Our approach analyzes morphological features at various spatial scales, efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details.
  arXiv  Detail & Related papers  (2024-04-01T00:23:22Z)
• Interpretable HER2 scoring by evaluating clinical Guidelines through a weakly supervised, constrained Deep Learning Approach [0.0]
  This paper focuses on the interpretability of HER2 scoring by a pathologist. We propose a semi-automatic, two-stage deep learning approach that directly evaluates the clinical HER2 guidelines. We achieve a performance of 0.78 in F1-score on the test set while keeping our model interpretable for the pathologist.
  arXiv  Detail & Related papers  (2022-11-17T14:28:35Z)
• Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging [62.346649719614]
  preoperative discrimination between T2 and T3 stages is arguably both the most challenging and clinically significant task for rectal cancer treatment. We present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes.
  arXiv  Detail & Related papers  (2022-09-13T07:10:14Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Automated Scoring System of HER2 in Pathological Images under the Microscope [3.689994643251581]
  We propose a HER2 automated scoring system that strictly follows the HER2 scoring guidelines. The proposed system will be embedded in our Thorough Eye platform for deployment in hospitals.
  arXiv  Detail & Related papers  (2021-10-21T01:38:35Z)
• Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images [49.1861463923357]
  We propose an uncertainty-guided dual-consistency learning network (UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images. Our proposed UDC-Net improves the fully supervised method by 6.3% in Dice and outperforms other competitive semi-supervised approaches by significant margins.
  arXiv  Detail & Related papers  (2021-04-07T16:23:35Z)
• Microscope Based HER2 Scoring System [37.747953235497256]
  We propose a real-time HER2 scoring system, which follows the HER2 scoring guidelines to complete diagnosis. Unlike the previous scoring systems based on whole-slide imaging, our HER2 scoring system is integrated into an augmented reality microscope.
  arXiv  Detail & Related papers  (2020-09-15T01:44:39Z)
• A multicenter study on radiomic features from T$_2$-weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics [47.187609203210705]
  2D and 3D T$$-weighted images of a pelvic phantom were acquired on three scanners. repeatability and repositioning of radiomic features were assessed.
  arXiv  Detail & Related papers  (2020-05-14T09:24:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.