arXiv AI
Robust Lightweight Deep Learning Models for Oral Cancer Screening
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arXiv AI
Aug 12
Energy and Performance Benchmarking of Deep Learning Models for Breast Cancer Detection
arXiv:2608. 09996v1 Announce Type: cross Abstract: Recent advances in machine learning have greatly improved breast cancer detection, enabling more accurate and timely diagnosis.
arXiv AI
Jul 8
The Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology
arXiv:2607. 06531v1 Announce Type: new Abstract: - Objective: Multimodal deep learning models in oncology are currently limited by monolithic designs that rigidly couple data ingestion, clinical routing, and artificial intelligence (AI) inference.
arXiv AI
4d ago
WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care
WoundAmbit is a framework that evaluates state‑of‑the‑art deep learning models for wound segmentation, benchmarking general‑purpose vision, medical imaging, and public wound challenge methods under standardized training, augmentation, and cross‑validation. The study finds transformer‑based TransNeXt offers the best generalizability, while all models run at least one image per second on a CPU and show strong interpretability and expert‑approved mask quality. The framework also introduces a reference‑object approach to convert masks into clinically relevant wound size estimates, demonstrating close alignment with expert annotations and integration into a custom telehealth system.
arXiv Computer Vision
Sep 4
Solving the Needle-in-a-Haystack Problem in Mammography Vision-Language Model with Differentiable Subset Sampling
The paper introduces TopKSigLIP, a vision‑language model tailored for mammography that tackles two key challenges: high‑resolution imaging and homogeneous radiology reports. It replaces standard CLIP training with a TopK‑Patch module that selects sparse high‑resolution patches likely to contain lesions, and a Sup‑sigmoid loss that uses soft labels from structured data instead of contrastive loss. TopKSigLIP outperforms existing open‑source mammography and general medical VLMs on zero‑shot tasks such as density assessment, BI‑RADS classification, finding subtyping, and cancer prediction, while also providing better lesion localization than Grad‑CAM.
arXiv Machine Learning
Jul 14
BiLoG-Net: A Bi-Context Location-Guided Network for Breast Mass Segmentation and Malignancy Classification in Mammography
arXiv:2607. 10188v1 Announce Type: cross Abstract: Breast cancer remains the most commonly diagnosed malignancy among women worldwide, yet accurate detection and characterization of breast masses in mammography remain challenging due to subtle intensity variations, heterogeneous tissue densities, and indistinct lesion boundaries that complicate radiological interpretation.
arXiv AI
Jul 16
Beyond Backbone Backpropagation: A Decoupled Strategy for Efficient Transfer Learning
arXiv:2607. 13043v1 Announce Type: cross Abstract: Deep learning models achieve state-of-the-art image classification but face deployment challenges due to computational costs and energy demands.