Neighborhood Attention Transformer Sharpens AI Segmentation of Polyps and Skin Lesions

A new artificial intelligence architecture that reads medical images the way a pathologist reads tissue slides, scanning small local neighborhoods before zooming out to grasp the whole picture, is reporting some of the strongest segmentation numbers yet achieved on two notoriously difficult clinical tasks: tracing colorectal polyps in colonoscopy video frames and outlining suspicious skin lesions in dermoscopy photographs. The system, called NAHFormer, was developed by Xuehui Yin, Haonan Li, Tianxiao Hou and Chao Tang at the School of Computer Science and Technology of Chongqing University of Posts and Telecommunications, and is described in a study published in the journal Applied Intelligence. Across seven publicly available benchmark datasets, the framework consistently outperformed state-of-the-art segmentation methods, reaching a mean Dice similarity coefficient of 0.821 on the challenging ETIS polyp dataset and 0.909 on the ISIC 2018 skin lesion dataset, two results that place it at the leading edge of what automated medical image analysis currently achieves on these benchmarks.

The clinical motivation behind the work is straightforward and consequential. Colorectal cancer remains one of the most common and deadly malignancies worldwide, and most of these cancers arise from polyps, small growths protruding from the lining of the colon that can be removed during routine colonoscopy before they turn malignant. Detecting and delineating those polyps accurately is therefore a direct determinant of whether a lesion is excised in time. Dermoscopy, meanwhile, is the dermatologist’s principal tool for the early detection of melanoma and other skin cancers, and the precise boundary of a lesion carries decisive diagnostic weight, informing decisions about biopsy, excision margins and follow-up. In both settings, clinicians today rely on manual or semi-manual outlining of lesions in images, a process that is slow, subjective and prone to inter-observer variability. An algorithm that could reliably draw those contours automatically would not only save expert time but could also flag subtle lesions that tired human eyes might miss.

Getting a computer to draw those contours, however, has proven remarkably stubborn. Polyps in endoscopic images vary enormously in morphology, size and appearance; some are flat and barely distinguishable in color from the surrounding mucosa, others sit at the edge of the frame, partially obscured by specular highlights, bubbles, or motion blur. Skin lesions present a parallel problem, with irregular, sometimes fading boundaries that blend into healthy skin. Early deep learning solutions, most famously the U-Net family of convolutional networks, achieved strong results by repeatedly downsampling and upsampling images and stitching fine local texture onto coarse semantic context. But convolutional kernels see only a small window at a time, so these networks struggle to connect distant parts of an image into a coherent global understanding, and they often produce ragged or leaking boundaries around small or low-contrast objects. Transformers, which use self-attention to let every pixel consult every other pixel, solved the long-range dependency problem but introduced a new one: the computational cost of global attention grows quadratically with the number of image tokens, making naive transformer segmentation expensive and sometimes imprecise at fine scales.

NAHFormer’s answer begins at the front of the network. The framework employs a pyramid-structured Mix Transformer, or MiT, encoder, a hierarchical backbone in which the image is progressively broken into larger patches through successive stages, producing a ladder of feature maps that descend in spatial resolution while ascending in semantic richness. Early stages capture delicate texture and fine edges, essential for tracing the exact rim of a polyp or lesion, while later stages encode the broader context needed to say, with confidence, that a given blob is a lesion at all. This multi-scale representation, the authors argue, is what underpins the model’s generalization capability, allowing it to cope with the extraordinary diversity of lesion appearances it encounters across different patients, cameras, imaging conditions and anatomical sites rather than overfitting to the quirks of any single dataset.

The first bespoke innovation sits above that encoder: a Cross-Resolution Semantic Perception, or CRSP, module. Its job is to make different layers of the pyramid talk to each other in a structured way, integrating semantic information across multiple resolutions so that the coarse layers’ understanding of what a lesion is can guide the fine layers’ placement of its boundary. The module performs this cross-resolution conversation through neighborhood attention, an attention mechanism in which each token attends not to the entire image but only to a small window of its nearest neighbors. That locality restriction, borrowed from the neighborhood attention transformer introduced by computer vision researchers in 2023, dramatically reduces computation compared with global self-attention while retaining most of the discriminative power, because in dense prediction tasks the pixels most relevant to resolving a given boundary are typically its immediate surroundings. The practical payoff, according to the team, is precise delineation of lesion contours and boundaries, precisely the capability where earlier transformer models have tended to stumble.

The second innovation, a Hierarchical Feature Fusion, or HFF, module, tackles a quieter but equally corrosive problem: redundancy. In multi-scale architectures, as features from different levels are combined, the same information is often carried forward repeatedly, and low-level detail can be swamped or diluted by what are effectively duplicate signals from coarser scales. The HFF module progressively aggregates the multi-scale feature hierarchy in a stepwise fashion, deliberately suppressing redundant information as it fuses, so that the final segmentation head receives a cleaner, more informative summary of the image. This redundancy-aware aggregation, combined with the boundary-sensitive attention of the CRSP module, is what the authors describe as the framework’s distinctive pairing of redundancy-aware and boundary-aware capabilities, a combination intended to raise accuracy without inflating the network into an unwieldy behemoth.

The experimental evidence spans the field’s most widely used public benchmarks. On the polyp side, the team evaluated NAHFormer on five colonoscopy datasets: Kvasir, CVC-ClinicDB, CVC-ColonDB, EndoScene and ETIS. These sets differ substantially in acquisition conditions and difficulty; CVC-ClinicDB contains relatively clean and well-lit frames, while ETIS is widely regarded as the sternest test, composed of small, poorly contrasted polyps that routinely drag down the scores of models that excel elsewhere. On the dermatology side, the framework was tested on the ISIC 2017 and ISIC 2018 skin lesion archives, curated through the International Skin Imaging Collaboration’s annual challenges for melanoma detection. The pattern of results is telling: NAHFormer did not merely top the leaderboard on the easy datasets but held its advantage on the hard ones, achieving its reported mean Dice score of 0.821 on ETIS and 0.909 on ISIC 2018, indicating that its architectural choices translate into genuine robustness rather than dataset-specific tuning.

The Dice coefficient, the metric at the center of these comparisons, measures the overlap between the algorithm’s predicted segmentation and the ground truth annotated by experts, ranging from zero for no overlap to one for perfect agreement, so the difference between a middling model and a strong one often comes down to whether the predicted boundary clips a sliver off the lesion or lets background tissue bleed in. Success on ETIS, where polyps are small and faintly demarcated, suggests that the neighborhood attention scheme is doing exactly what it was designed to do, pinning down delicate boundaries without the blurring that coarse feature fusion can introduce. The strong ISIC result reinforces the point from the opposite direction, since skin lesion boundaries are often biologically diffuse, fading gradually into surrounding skin, and demand a different kind of perceptual finesse. A single architecture scoring highly across both regimes hints at a level of adaptability that previous task-specific models, whether convolutional designs like PraNet or hybrid transformer convolutional systems such as TransFuse, Swin-UNet, MissFormer and H2Former, have generally had to trade off against one another.

The broader significance of the work lies in what it suggests about the trajectory of clinical AI. Rather than choosing between the precision of convolutional networks and the global reasoning of transformers, the Chongqing team’s design stitches the two virtues together, using a hierarchical transformer backbone, locality-restricted attention and progressive fusion to keep both computation and accuracy in a clinically usable range. All of the datasets used in the study are publicly available, which means other groups can immediately stress-test the claims and build on the architecture, and the work was supported in part by the National Natural Science Foundation of China under Grant 61701060 and by a Chongqing Graduate Scientific Research Innovation Project under Grant CYS25470. The researchers frame the system as a step toward segmentation tools that could eventually assist physicians in real time, flagging and outlining lesions as images are captured. For now, the result stands as a demonstration that rethinking where an attention mechanism should look, and what it should ignore, can push the frontier of medical image analysis forward on the benchmarks that matter.

Subject of Research: A transformer-based deep learning framework for segmenting colonoscopic polyps and dermoscopic skin lesions in medical images.

Article Title: NAHFormer: Neighborhood self-attention and hierarchical feature fusion transformer for image medical segmentation

Article References: Yin, X., Li, H., Hou, T., & Tang, C. (2026). NAHFormer: Neighborhood self-attention and hierarchical feature fusion transformer for image medical segmentation. Applied Intelligence, 56(15), Article 430. https://doi.org/10.1007/s10489-026-07474-w

Image Credits: AI Generated

DOI: 10.1007/s10489-026-07474-w

Keywords: medical image segmentation, transformer, neighborhood attention, polyp segmentation, skin lesion segmentation, colonoscopy, dermoscopy, deep learning, computer vision, multi-scale feature fusion, Dice score, Applied Intelligence