arXiv:2606. 07553v1 Announce Type: cross Abstract: The emergence of machine learning and deep learning has revolutionized the efficiency of diagnostic, therapeutic, and administrative systems in healthcare.
arXiv:2603.26483v2 Announce Type: replace Abstract: Medical edge-AI systems must operate under a difficult tension: delivering reliable diagnostic inference while running on devices with limited batt...
arXiv:2607. 26333v1 Announce Type: cross Abstract: Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment.
Background: Early prediction of distant metastasis (DM) risk in head and neck cancer (HNC) can enable timely interventions that may improve treatment outcomes. Many current machine learning methods rely on prior knowledge of the region of interest such as tumor segmentations, which require expert knowledge, is time-consuming and introduces user-dependent variability.
arXiv:2607. 23883v1 Announce Type: cross Abstract: In this paper, we examine the difficulties of using standard techniques for medical image classification due to long-tailed distributions (wherein rarer conditions have very few samples) resulting in bias towards diagnosing common diseases and away from rarer diseases.
arXiv:2608.21571v1 Announce Type: new Abstract: Lung cancer remains a leading cause of cancer-related mortality worldwide, and early diagnosis is critical for improving survival. However, early-stage...
arXiv:2607. 05628v1 Announce Type: cross Abstract: Accurate and efficient classification of thoracic diseases in chest X-ray (CXR) images is crucial for timely diagnosis and treatment.
Posted by Dave Steiner, Clinical Research Scientist, Google Health, and Rory Pilgrim, Product Manager, Google Research There’s a worldwide shortage of access to medical imaging expert interpretation across specialties including radiology , dermatology and pathology . Machine learning (ML) technology can help ease this burden by powering tools that enable doctors to interpret these images more accurately and efficiently.
arXiv:2607. 28538v1 Announce Type: cross Abstract: Classifying pathological scars from clinical photographs requires distinguishing keloids from hypertrophic scars despite limited expert-labeled data and substantial acquisition variation across hospitals.
arXiv:2408.07988v3 Announce Type: replace Abstract: Despite significant research efforts and advancements, cancer remains a leading cause of mortality. Early cancer prediction has become a crucial fo...
arXiv:2609.26384v1 Announce Type: new Abstract: Medical image interpretation is high-volume and time-consuming, and while AI interpretation can reduce workload, fully autonomous deployment carries po...
arXiv:2606. 04453v1 Announce Type: cross Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis.
arXiv:2608. 16198v1 Announce Type: cross Abstract: Dermatology models face distribution shifts in teledermatology settings, where submitted images differ from the training data in lighting, angle, distance, focus, and framing.