Deep Learning Model for Cell Nuclei Segmentation and Lymphocyte Identification in Whole Slide Histology Images
Bibliographic record
Abstract
Anti-cancer immunotherapy dramatically changes the clinical management of many types of tumours towards less harmful and more personalized treatment plans than conventional chemotherapy or radiation. Precise analysis of the spatial distribution of immune cells in the tumourous tissue is necessary to select patients that would best respond to the treatment. Here, we introduce a deep learning-based workflow for cell nuclei segmentation and subsequent immune cell identification in routine diagnostic images. We applied our workflow on a set of hematoxylin and eosin (H&E) stained breast cancer and colorectal cancer tissue images to detect tumour-infiltrating lymphocytes. Firstly, to segment all nuclei in the tissue, we applied the multiple-image input layer architecture (Micro-Net, Dice coefficient (DC) $0.79\pm 0.02$). We supplemented the Micro-Net with an introduced texture block to increase segmentation accuracy (DC = $0.80\pm 0.02$). We preserved the shallow architecture of the segmentation network with only 280 K trainable parameters (e.g. U-net with ∼1900 K parameters, DC = $0.78\pm 0.03$). Subsequently, we added an active contour layer to the ground truth images to further increase the performance (DC = $0.81\pm 0.02$). Secondly, to discriminate lymphocytes from the set of all segmented nuclei, we explored multilayer perceptron and achieved a 0.70 classification f-score. Remarkably, the binary classification of segmented nuclei was significantly improved (f-score = 0.80) by colour normalization. To inspect model generalization, we have evaluated trained models on a public dataset that was not put to use during training. We conclude that the proposed workflow achieved promising results and, with little effort, can be employed in multi-class nuclei segmentation and identification tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".