The paper introduces a lightweight attention-based CNN (CNN-Attention) for binary skin cancer classification that uses channel attention to highlight informative dermoscopic regions while keeping computational demands low. Trained on a dataset of 2,637 images, the model achieved 87 % accuracy, 0.9475 ROC AUC, and 0.9430 PR AUC, matching MobileNet’s performance with roughly half the parameters and storage. Saliency maps are employed to provide explainability by visualizing the image areas that influence predictions.
By Sanjana Islam Kasfia, Md. Ismiel Hossen Abir, AKM Bahalul Haque, Abir Mahmud Shahariar
The paper introduces S$^3$F-Net, a dual‑branch network that fuses spatial and spectral representations for medical image classification. It combines a deep spatial CNN with a shallow spectral encoder, SpectraNet, which uses a learnable SpectralFilter layer to process the full Fourier spectrum efficiently. Evaluated on four medical imaging datasets, S$^3$F-Net consistently outperforms spatial‑only baselines, achieving state‑of‑the‑art accuracy on BRISC2025 and surpassing deeper models on the Chest X‑Ray Pneumonia dataset.
By Md. Saiful Bari Siddiqui, Mohammed Imamul Hassan Bhuiyan
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.
CLARiTy is a vision transformer model that jointly performs multi‑label classification and weakly‑supervised localization of chest X‑ray pathologies. It uses class‑specific tokens to produce attention maps and a SegmentCAM module to refine foreground segmentation with anatomical priors. Trained on NIH ChestXray14 image‑level labels and distilled from a ConvNeXtV2 teacher, CLARiTy achieves competitive classification and strong localization, especially for small pathologies, outperforming prior methods by up to 50.7% relative Macro IoU accuracy.
By John M. Statheros, Hairong Wang, Richard Klein
arXiv:2608.06205v2 Announce Type: replace
Abstract: Multispectral object detection combines visible and thermal imagery to improve perception under challenging illumination and environmental conditio...
By Nima Hatami, Karim Faez, Saeed Sharifian, Hamidreza Amindavar
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.
By Abu Fatema Mohammad Abdun Noor, Md Imam Ahasan, Md Samiul Ahasan, Kah Ong Michael Goh, S M Hasan Mahmud, Raihana Zannat