Aug 2026· British Journal of Dermatology· 0 citations
Medicine
TL;DR
It is proposed that BRAF amplification in these cases more likely serves as a diagnostic corrective marker for misclassified dermal metastases, rather than a true prognostic indicator for isolated PDM.
Abstract
We address a fundamental ground-truth fallacy in the study's design: utilizing admittedly imperfect clinical diagnoses as the absolute standard to train classification models creates a circular logic. We propose that BRAF amplification in these cases more likely serves as a diagnostic corrective marker for misclassified dermal metastases, rather than a true prognostic indicator for isolated PDM.
Author’s response to ‘High-dimensional overfitting and noisy diagnostic labels: reconsidering the role of BRAF copy number in primary dermal melanoma’ by Zhang et al.
Peinan Zhao, A. Papenfuss, M. Shackleton· British Journal of Dermatolo...· 0 citations
This article comments on a recent study of an artificial intelligence (AI) tool for detection of CT-occult pancreatic cancer. In a multiinstitutional dataset, the tool detected such cancers with a median 475-day lead time, outperforming subspecialty-trained radiologists. Future studies should evaluate the model prospectively to assess generalizability and reduce false-positives.
Govind Matay, Richard Tsai· AJR. American journal of roe...· 0 citations
MD-Mamba integrates state-space modeling with multi-scale dilated convolutions with multi-scale dilated convolutions and enables efficient, interpretable image biomarkers for breast cancer pathology.
Gengxun Liu, Shengquan Luo, Can Wu et al.· Translational Oncology· 0 citations
TFE3‐rearranged renal cell carcinoma (TFE3‐rRCC) is a rare, aggressive subtype that predominantly affects adolescents and young adults. Its marked morphologic heterogeneity can delay recognition and downstream confirmatory testing.
Yu-Hang Chen, Quanhui Xu, Haohua Yao et al.· Cancer Medicine· 0 citations
Skin cancer incidence is rising globally, and early accurate classification of dermoscopic lesions is critical for improving patient outcomes, particularly for melanoma where delayed detection drastically worsens prognosis. This work presents a comprehensive framework for multiclass skin lesion classification on the ISIC 2019 benchmark, which comprises eight diagnostic categories and a ninth unknown out-of-distribution (OOD) class in the test set. Our system addresses three interlinked challenges: extreme class imbalance, robust integration of patient metadata, and reliable OOD rejection. The architecture utilises an EfficientNet-B4 backbone along with a novel Adaptive Metadata Gating (AMG) module that learns imageconditioned gates bounded by an explicit ceiling in order to prevent shortcut reliance on metadata. Training incorporates an asymmetric focal loss with a double penalty for malignant false negatives, a hierarchically annealed auxiliary malignancy loss, class-aware Mixup/CutMix augmentation, Shades-of-Gray colour constancy, Exponential Moving Averaging, and Stochastic Weight Averaging. HAM10000 data supplements minority classes. Five independently trained EMA-checkpointed models are combined via softmax-temperature weighted logit-space averaging with fiveview test-time augmentation. An entropy-plus-gate composite uncertainty score drives calibrated per-fold threshold tuning for OOD rejection. On the official ISIC 2019 test set, the ensemble achieves balanced accuracy of 57.2%, macro AUC of 0.924, and macro specificity of 95.4%. On a fresh 20% stratified hold-out of ISIC training data, balanced accuracy rises to 80.3% and macro AUC to 0.975, confirming strong in-distribution discriminative capacity, with the test-set gap attributable to domain shift and the presence of unlabelled OOD samples.
S. V., Chris George Shibu, Gali Manish Kumar· 2026 International Conferenc...· 0 citations
Traditionally, cytology expertise has been equated with professional experience. However, the transition to whole‐slide imaging and artificial intelligence (AI) necessitates a shift from exhaustive screening to rapid verification. The goal of this study was to identify cognitive biomarkers associated with diagnostic accuracy and evaluate their modifiability.
Naoya Abe, Yukari Nishimura, K. Yamashita et al.· Cancer Cytopathology· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
