2026· Optica Biophotonics Congress 2026· pp. JT4A.44· 0 citations· 2 references
TL;DR
VDSR networks enhance breast histopathological image resolution, but performance is significantly higher for malignant than benign lesions, necessitating optimization.
Abstract
VDSR networks enhance breast histopathological image resolution, but performance is significantly higher for malignant (PSNR=38dB, SSIM=0.992) than benign lesions (PSNR=36dB, SSIM=0.980), necessitating optimization. Prospective studies on segmentation and classification pipelines will support scalable AI-assisted pathology.
MD-Mamba integrates state-space modeling with multi-scale dilated convolutions with multi-scale dilated convolutions and enables efficient, interpretable image biomarkers for breast cancer pathology.
Gengxun Liu, Shengquan Luo, Can Wu et al.· Translational Oncology· 0 citations
Highlights • PCMamba integrates Partial Convolution with State Space Models for lung cancer classification.• Achieves 94.17% accuracy and 99.28% AUC on the LungHist700 dataset.• Dual-branch design captures both local textures and long-range tissue dependencies.• Reduces computational cost by 75% while maintaining state-of-the-art performance.
Xiao-Lin Wang, Bo Chen, Yu-Han Chen et al.· Translational Oncology· 0 citations
Speckle patterns in ultrasound images often obscure anatomical details, leading to diagnostic uncertainty. Recently, various deep learning‐based techniques have been introduced to effectively suppress speckle; however, their high computational costs pose challenges for low‐resource devices, such as portable ultrasound systems.
Hyunwoo Cho, Jae-Shin Lee, Jongsoo Lee et al.· Medical Physics (Lancaster)· 0 citations
This study benchmarks colonoscopy image reconstruction. We introduce a Latent Bank method to improve standard encoders, but High-Fidelity GAN Inversion achieves superior quality (FID = 22.12, MS-SSIM > 0.91), proving the best for colonoscopy images.
Vu Quang Huy Tran, C. Angelina, S. Vyas et al.· Digital Holography and 3-D I...· 0 citations
Breast cancer is one of the leading causes of death in women and early diagnosis and correct diagnosis is important. Although ultrasound imaging is widely used due to its non-invasive nature and is low cost, suitable for real time clinical assessment, lesion segmentation is challenging because of speckle noise, low contrast, shadowing, fuzzy boundaries and variations in lesion size and shape. This paper proposes an improved DeepLabv3+ segmentation framework using EfficientNetB4 as the encoder to localize the breast lesions in ultrasound images. It was chosen because of its ability to achieve a high accuracy to computation ratio via compound scaling and can also be used to generate rich multi-scale feature representations, using fewer parameters than its heavier counterparts. The public Breast Ultrasound Images (BUSI) dataset is subjected to image resizing, normalization, Contrast Limited Adaptive Histogram Equalization (CLAHE), synchronized augmentation and pixel-wise mask learning during the proposed pipeline. CLAHE has the effect of increasing the contrast of the lesion boundary region in it and decreasing the ambiguity of the boundaries prior to feature extraction which enhances the interpretability of the segmentation output. Experimental analysis shows an accuracy of 94.57%, precision of 84.11%, recall of 42.59% and F1-score of 56.55%. While recall is still moderate for difficult lesions, the model shows stable convergence, high pixel-level accuracy and clinically useful lesion localization. The new study also provides more insight into the split of the data set and the composition of the data set and the comparison with other latest segmentation models.
B. B. Jayasingh, N. Monika· 2026 4th International Confe...· 0 citations
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