A Comparison of Autoencoders and Variational Autoencoders for Anomaly Detection in Dermoscopic Skin Lesion Imagery
Bibliographic record
Abstract
Early detection and diagnosis of skin abnormalities are crucial for effective treatment and management of skin diseases. This paper explores the application of deep learning techniques for skin tissue analysis, focusing on the detection of abnormalities from dermoscopic images. Unsupervised learning methods such as Autoencoders (AE) and Variational Autoencoders (VAE) save resources by eliminating the need for labeled data, making them more efficient and scalable than supervised learning. We compare the performance of AE and VAE architectures in developing a robust model capable of distinguishing between benign and malignant skin lesions. This study uses the MNIST HAM10000 dataset of 10,015 dermoscopic images, divided into seven classes that represent benign and malignant diagnostic categories. The data set was divided into benign (normal) and malignant (anomalous) cases. The models were trained to learn features of the normal data and generate reconstructions of these images. An optimal decision boundary is chosen to classify images as benign or malignant based purely on their reconstruction error. Experimental results, averaged over 30 training runs, demonstrate that the AE outperforms the VAE in accuracy (71.84/% vs. 67.79/%), F1-score (73.03/% vs. 67.33/%), and FNR (23.76/% vs. 33.58/%). An evaluation of Autoencoder reconstructions using Shapley Additive Explanations (SHAP) analysis was performed to visualize and identify the pixel-wise attributions of anomalies thereby providing insight as to which factors most influenced model reconstruction and, therefore, decisions in label assignment for each AE approach. Some general patterns were clear, particularly regarding high attribution assignments to centralized pixel regions of the most anomalous images. These findings suggest that the AE architecture is promising for automated skin cancer detection, which can lead to more accurate and timely diagnoses and improved patient outcomes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".