Deep Learning-Based Nuclei Segmentation and Melanoma Detection in Skin Histopathological Image Using Test Image Augmentation and Ensemble Model
Why this work is in the frame
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Bibliographic record
Abstract
Histopathological images play a crucial role in diagnosing skin cancer. However, due to the very large size of digital histopathological images (typically in the order of billion pixels), manual image analysis is tedious and time-consuming. Therefore, there has been significant interest in developing Artificial Intelligence (AI)-enabled computer-aided diagnosis (CAD) techniques for skin cancer detection. Due to the diversity of uncertain cell boundaries, automated nuclei segmentation of histopathological images remains challenging. Automating the identification of abnormal cell nuclei and analyzing their distribution across multiple tissue sections can significantly expedite comprehensive diagnostic assessments. In this paper, a deep neural network (DNN)-based technique is proposed to segment nuclei and detect melanoma in histopathological images. To achieve a robust performance, a test image is first augmented by various geometric operations. The augmented images are then passed through the DNN and the individual outputs are combined to obtain the final nuclei-segmented image. A morphological technique is then applied on the nuclei-segmented image to detect the melanoma region in the image. Experimental results show that the proposed technique can achieve a Dice score of 91.61% and 87.9% for nuclei segmentation and melanoma detection, respectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it