Detection of Cancer-Associated Nuclei in Histopathology Images Using Deep Convolutional Neural Networks

This paper presents a deep-learning pipeline for detecting nuclei-positive regions in hematoxylin and eosin (H&E) histopathology images. The study uses the public CryoNuSeg segmentation dataset, distributed through Kaggle, which contains 30 frozen tissue sections from ten human organs together with more than 8,000 manually annotated nuclei. Images were normalized, divided into smaller patches, and paired with binary masks so that the model learned a pixel-wise segmentation task rather than a slide-level diagnosis task. On the held-out evaluation set, the final model reached 0.91 pixel accuracy, with class-wise precision and recall of 0.96 and 0.92 for background pixels and 0.73 and 0.86 for nuclei pixels. The corresponding macro F1-score was 0.87, the weighted F1-score was 0.91, and the ROC–AUC score was 0.95. Qualitative comparisons showed that predicted masks tracked the overall location and shape of many nuclei-rich regions, although false positives remained in crowded or weak-contrast tissue. The results suggest that a compact convolutional neural network can provide a useful nuclei-segmentation baseline on public histopathology data and can serve as a starting point for more rigorous computational pathology work.