arXiv:2608. 03079v1 Announce Type: cross Abstract: Breast core needle biopsy (CNB) is central to breast cancer diagnosis yet remains challenging because limited tissue sampling, lesion heterogeneity, and subtle morphologic overlap can obscure subtype distinctions.
arXiv:2511. 15968v2 Announce Type: replace-cross Abstract: External validation of breast ultrasound segmentation models remains limited because internal train--test splits do not capture domain shifts across imaging systems, acquisition protocols, and patient populations.
arXiv:2603. 01250v2 Announce Type: replace-cross Abstract: Breast cancer is the most frequently diagnosed malignancy among women worldwide and a leading cause of cancer-related mortality.
arXiv:2608. 07857v1 Announce Type: cross Abstract: Foundation models provide transferable CT representations, but predictions based directly on these embeddings are difficult to interpret.
This study evaluates the classification accuracy of six modern deep‑learning architectures—VGG19, ResNet50, GoogleNet, ConvNeXt, EfficientNet, and Vision Transformers—on breast ultrasound images categorized by BI‑RADS. Using 2,945 training images and 936 validation images from 1,540 patients, the models were tested in full fine‑tuning, linear evaluation, and training‑from‑scratch settings. The best performance was achieved with full fine‑tuning, yielding 76.39 % accuracy and a 67.94 % F1 score.
MagViT is an interpretable multi‑magnification transformer that classifies breast histopathology images by extracting representations from four BreakHis magnifications (40X, 100X, 200X, 400X) and fusing them with a learnable, scale‑gated mechanism that can mask missing scales. The model selects the most accurate architectural branch at the patient level using five‑fold cross‑validation, achieving high performance on BreakHis (mean image accuracy 0.9191, patient accuracy 0.9643, macro‑F1 0.9042) and demonstrating preliminary cross‑dataset generalization on BUSI and IDC. Grad‑CAM visualizations confirm that the network focuses on diagnostically relevant regions across magnifications.
arXiv:2607. 12075v1 Announce Type: cross Abstract: Background: Deep learning models can classify thyroid nodules on ultrasound, but reliable clinical decision support also requires calibrated probabilities, uncertainty estimation, and selective referral, particularly under dataset shift.
Breast DCE-MRI AI is increasingly being explored for breast-level classification of no-lesion, benign, and malignant findings, beyond conventional lesion-centered diagnosis. Within this broader diagnostic scope, however, patient-specific background variability remains a major source of imaging confounding across classification tasks.
TAM-Chain is a multi‑scale thyroid cytology classification framework that uses Absorbing Markov Chains and Shannon Entropy to quantify uncertainty and dynamically decide when to stop processing and refer to a specialist. It processes images at 10×, 20×, and 40× magnifications, achieving a Macro F1 score of 0.9741 on an internal test set with a 0 % false‑negative rate, and maintains a Macro F1 of 0.7026 on an external validation set with severe domain shift. The method outperforms single‑magnification baselines by adaptively adjusting stopping steps and triggering specialist referrals, thereby reducing critical diagnostic errors.
The paper introduces FCA‑Guided Counterfactual (FCA‑CF) explanations for multi‑modal breast cancer diagnosis, leveraging a Formal Concept Analysis lattice as a hard structural constraint to search for counterfactuals. On the TCGA‑BRCA dataset, FCA‑CF achieves perfect validity (100% prediction flips), the lowest average feature changes (2.37), and competitive proximity (0.900) compared to four other methods. Ablation studies show the lattice constraint drives sparsity, while a greedy refinement phase further improves results.
arXiv:2609.14676v1 Announce Type: new Abstract: Purpose: To compare four multimodal large language models (MLLMs) with radiologists of varying expertise in breast density assessment, BI-RADS assessme...
arXiv:2606. 13135v1 Announce Type: cross Abstract: Purpose.
The paper introduces FCA‑Guided Counterfactual (FCA‑CF) explanations for multi‑modal breast cancer diagnosis, leveraging a Formal Concept Analysis lattice as a hard structural constraint to generate counterfactuals. On the TCGA‑BRCA dataset, FCA‑CF achieves perfect validity (100% prediction flips), the lowest average feature changes (2.37), and competitive proximity (0.900), outperforming four established counterfactual methods. Ablation studies show the lattice constraint and a greedy refinement phase are key to its sparsity and validity.