A Leakage-Aware Comparative Benchmark of Machine Learning, Deep Learning, and Transformer Models for Reliable Leukemia Detection

• URL: http://arxiv.org/abs/2606.24944v1
• Date: Tue, 23 Jun 2026 04:53:49 GMT
• Title: A Leakage-Aware Comparative Benchmark of Machine Learning, Deep Learning, and Transformer Models for Reliable Leukemia Detection
• Abstract summary: Automated classification of acute lymphoblastic leukemia (ALL) from peripheral blood smear images has often reported near-perfect performance on the C-NMC 2019 dataset.<n>We show that such results can be inflated by patient-level data leakage caused by random image-level partitioning.<n>We establish a leakage-aware benchmark under a strict subject-disjoint protocol.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated classification of acute lymphoblastic leukemia (ALL) from peripheral blood smear images has often reported near-perfect performance on the C-NMC 2019 dataset. We show that such results can be inflated by patient-level data leakage caused by random image-level partitioning, where cells from the same subject may appear in both training and test folds. We establish a leakage-aware benchmark under a strict subject-disjoint protocol, comparing LightGBM, RBF-SVM, EfficientNet-B0, EfficientNet-B1, and ViT-Tiny. Models are developed using three subject-disjoint folds from 73 subjects and evaluated on an external preliminary-phase test set of 1,867 images from 28 unseen subjects with zero patient overlap. Beyond discrimination, we assess calibration using expected calibration error, Brier score, and temperature scaling. Under honest evaluation, EfficientNet-B1 achieves the best performance, with AUROC 0.913, sensitivity 0.87, specificity 0.80, and calibrated ECE 0.024. Frozen-feature classifiers and ViT-Tiny show high sensitivity but poor specificity, indicating a tendency to over-predict the malignant class. A random-versus-subject-disjoint ablation shows that random splitting inflates AUROC by about 0.04 even in the conservative frozen-feature setting. These findings caution against image-level evaluation on C-NMC 2019 and provide a reproducible, calibration-aware benchmark for future work.

Related papers

• TRIAGE: Risk-Controlled Pseudo-Label Admission for Annotation-Efficient Semi-Supervised Retinal OCT Classification [0.6213771671016098]
  This paper proposes TRIAGE, a risk-controlled semi-supervised framework for OCT scans classification.<n>On the dataset from Noor Eye Hospital (16,822 B-scans, 161 patients, and 554 volumes), TRIAGE demonstrates 89.66% scan-level accuracy, 0.8805 macro-F1, 0.9641 macro-AUC, and an 8.34% under-grading rate.
  arXiv  Detail & Related papers  (2026-08-14T14:06:48Z)
• Trustworthy Medical Segmentation: Uncertainty-Aware U-Net Evaluation Under Clinical Image Degradation [0.0]
  We present a framework for evaluating uncertainty-aware segmentation under clinical degradation.<n>We measure segmentation accuracy, calibration, failure detection, and selective prediction coverage.<n>These results argue for uncertainty-aware inference as a practical safety layer in physician-in-the-loop radiology.
  arXiv  Detail & Related papers  (2026-07-22T06:35:33Z)
• CaresAI at SMM4H-HeaRD 2026: Predicting TNM Staging [2.7998963147546143]
  This study aims to predict Tumor, Node, and Metastasis (TNM) stage labels independently, with the Cancer Genome Atlas (TCGA) pathology report as the sixth shared task of SMM4H-HeaRD 2026.<n>We explore both classical and deep learning approaches using Term Frequency-Inverse Document Frequency (TF-IDF) features and embeddings from ClinicalBERT, BioBERT, and PubMedBERT.<n>Our results show that individual embeddings perform similarly to the TNM label classification, while their combination improves its predictive ability.
  arXiv  Detail & Related papers  (2026-07-03T16:25:35Z)
• Robust Multicentre Detection and Classification of Colorectal Liver Metastases on CT: Application of Foundation Models [11.274035647041762]
  We developed a foundation model-based AI pipeline for patient-level classification and lesion-level detection of CRLM on CT.<n> UMedPT achieved the best performance and was fine-tuned with a head for classification and an FCOS-based head for lesion detection.
  arXiv  Detail & Related papers  (2026-01-12T14:35:29Z)
• Improved cystic hygroma detection from prenatal imaging using ultrasound-specific self-supervised representation learning [0.18058404137575482]
  Cystic hygroma is a high-risk prenatal ultrasound finding that portends high rates of chromosomal abnormalities, structural malformations, and adverse pregnancy outcomes.<n>This study assesses whether ultrasound-specific self-supervised pretraining can facilitate accurate, robust deep learning detection of cystic hygroma in first-trimester ultrasound images.
  arXiv  Detail & Related papers  (2025-12-28T00:07:26Z)
• Transparent Early ICU Mortality Prediction with Clinical Transformer and Per-Case Modality Attribution [42.85462513661566]
  We present a lightweight, transparent multimodal ensemble that fuses physiological time-series measurements with unstructured clinical notes from the first 48 hours of an ICU stay.<n>A logistic regression model combines predictions from two modality-specific models: a bidirectional LSTM for vitals and a finetuned ClinicalModernBERT transformer for notes.<n>On the MIMIC-III benchmark, our late-fusion ensemble improves discrimination over the best single model while maintaining well-calibrated predictions.
  arXiv  Detail & Related papers  (2025-11-19T20:11:49Z)
• MSRANetV2: An Explainable Deep Learning Architecture for Multi-class Classification of Colorectal Histopathological Images [3.4859776888706233]
  Colorectal cancer (CRC) is a leading worldwide cause of cancer-related mortality.<n>Deep learning algorithms have become a powerful approach in enhancing diagnostic precision and efficiency.<n>We propose a convolutional neural network architecture named MSRANetV2, specially optimized for the classification of colorectal tissue images.
  arXiv  Detail & Related papers  (2025-10-28T07:22:34Z)
• Glaucoma Detection and Structured OCT Report Generation via a Fine-tuned Multimodal Large Language Model [1.0925680160683622]
  The model was evaluated on a held-out test set for three tasks: quality assessment, glaucoma detection, and RNFL thinning classification.<n>The model achieved high accuracy in identifying image quality issues and detecting glaucoma.
  arXiv  Detail & Related papers  (2025-10-01T22:37:28Z)
• A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler [49.03919553747297]
  We propose an AI-powered, real-time CoW auto-segmentation system capable of efficiently capturing cerebral arteries.<n>No prior studies have explored AI-driven cerebrovascular segmentation using Transcranial Color-coded Doppler (TCCD)<n>The proposed AAW-YOLO demonstrated strong performance in segmenting both ipsilateral and contralateral CoW vessels.
  arXiv  Detail & Related papers  (2025-08-19T14:41:22Z)
• CRTRE: Causal Rule Generation with Target Trial Emulation Framework [47.2836994469923]
  We introduce a novel method called causal rule generation with target trial emulation framework (CRTRE) CRTRE applies randomize trial design principles to estimate the causal effect of association rules. We then incorporate such association rules for the downstream applications such as prediction of disease onsets.
  arXiv  Detail & Related papers  (2024-11-10T02:40:06Z)
• Enhancing Diagnostic Reliability of Foundation Model with Uncertainty Estimation in OCT Images [41.002573031087856]
  We developed a foundation model with uncertainty estimation (FMUE) to detect 11 retinal conditions on optical coherence tomography ( OCT) FMUE achieved a higher F1 score of 96.76% than two state-of-the-art algorithms, RETFound and UIOS, and got further improvement with thresholding strategy to 98.44%. Our model is superior to two ophthalmologists with a higher F1 score (95.17% vs. 61.93% &71.72%)
  arXiv  Detail & Related papers  (2024-06-18T03:04:52Z)
• Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification [71.06194656633447]
  We establish an uncertainty-inspired open-set (UIOS) model, which was trained with fundus images of 9 retinal conditions. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set. UIOS correctly predicted high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images.
  arXiv  Detail & Related papers  (2023-04-08T10:47:41Z)
• The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations [59.48245489413308]
  We developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1.
  arXiv  Detail & Related papers  (2021-09-18T02:28:01Z)
• Vision Transformers for femur fracture classification [59.99241204074268]
  The Vision Transformer (ViT) was able to correctly predict 83% of the test images. Good results were obtained in sub-fractures with the largest and richest dataset ever.
  arXiv  Detail & Related papers  (2021-08-07T10:12:42Z)

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.