arXiv AI
Jul 21
Sequential Attention-based Sampling for Histopathological Analysis
arXiv:2507. 05077v5 Announce Type: replace-cross Abstract: Deep neural networks are increasingly applied in automated histopathology.
Distribution-based deep multiple instance learning for tumor proportion scoring in NSCLC
arXiv:2606. 27579v1 Announce Type: cross Abstract: Accurate assessment of tumor proportion score (TPS) in non-small cell lung cancer (NSCLC) is critical for treatment planning and prognosis.
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arXiv AI
1d ago
Comprehensive Benchmarking of Deep Learning Architectures for Lung Cancer Histopathology
arXiv:2608. 15915v1 Announce Type: cross Abstract: Lung cancer remains the leading cause of cancer-related mortality worldwide, while histopathological diagnosis is often affected by inter-observer variability and the substantial workload associated with manual slide examination.
arXiv Machine Learning
2d ago
XtraLight-MedMamba for Classification of Neoplastic Tubular Adenomas
arXiv:2602. 04819v5 Announce Type: replace-cross Abstract: Accurate risk stratification of precancerous polyps during routine colonoscopy screening is a key strategy to reduce the incidence of colorectal cancer (CRC).
arXiv AI
2d ago
CMCNet: Aligning Ultrasound Image Embeddings with Textual TI-RADS Representations for Fine-Grained Thyroid Classification
arXiv:2608. 13939v1 Announce Type: cross Abstract: Ultrasound is the primary imaging modality for assessing thyroid nodules, and the ACR TI-RADS framework standardizes diagnosis through five ultrasound feature categories that are aggregated into five risk levels (TR1-TR5).
arXiv Machine Learning
Jun 8
LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification
arXiv:2606. 06864v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become a standard paradigm for whole slide image (WSI) analysis in digital pathology, as it enables slide-level prediction without dense annotations.
arXiv AI
Jul 17
Pretraining Multiple Instance Learning Networks with Multi-Teacher Distillation from Pathology Slide Foundation Models
arXiv:2607. 14703v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become the main paradigm for whole-slide image (WSI) analysis in computational pathology.
Hugging Face Trending Papers
Jun 22
From Point Estimates to Distributions: GMM Pooling for MIL in Preterm Birth Prediction
Preterm birth (PTB) prediction can enable targeted surveillance and timely intervention, yet most ultrasound-based models use a single selected transvaginal ultrasound (TVUS) frame per patient despite routine exams acquiring multiple cervical images. We formulate PTB prediction as a multiple instance learning (MIL) problem, representing each patient as a variable-sized bag of TVUS images with a single outcome label.
arXiv Machine Learning
Jul 24
Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction
arXiv:2410. 00945v2 Announce Type: replace-cross Abstract: Gene-expression profiling is widely used in research and central to many areas of precision oncology, but remains costly and not universally accessible.
arXiv AI
Jul 21
GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
arXiv:2607. 18218v1 Announce Type: cross Abstract: Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data.
arXiv Machine Learning
Jun 10
TRAPS: Therapeutic Response Analysis via Pathway-informed Stratification
arXiv:2606. 09898v1 Announce Type: new Abstract: Cancer treatment planning requires decisions across multiple clinical dimensions at once.
arXiv AI
Jun 26
A Guideline-Aware AI Agent for Zero-Shot Target Volume Auto-Delineation
arXiv:2603. 09448v2 Announce Type: replace-cross Abstract: Delineating the clinical target volume (CTV) in radiotherapy involves complex margins constrained by tumor location and anatomical barriers.
arXiv AI
Jul 2
TRCGL-Net: A Long-Tailed Multi-Label Chest X-Ray Classification Framework with Generative Data Augmentation and Label Co-Occurrence Modeling
arXiv:2607. 00975v1 Announce Type: cross Abstract: Chest X-ray multi-label classification is a core task in intelligent medical imaging diagnosis.