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When an Image is Worth 1,024 x 1,024 Words: A Case Study in Computational Pathology

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arxiv 2312.03558 v1 pith:SSRBH6UK submitted 2023-12-06 cs.CV

Wenhui Wang , Shuming Ma , Hanwen Xu , Naoto Usuyama , Jiayu Ding , Hoifung Poon , Furu Wei This is my paper · ORCID
classification cs.CV
keywords gigapixellongvitimagescancercomputationcomputationalimagelongnet
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This technical report presents LongViT, a vision Transformer that can process gigapixel images in an end-to-end manner. Specifically, we split the gigapixel image into a sequence of millions of patches and project them linearly into embeddings. LongNet is then employed to model the extremely long sequence, generating representations that capture both short-range and long-range dependencies. The linear computation complexity of LongNet, along with its distributed algorithm, enables us to overcome the constraints of both computation and memory. We apply LongViT in the field of computational pathology, aiming for cancer diagnosis and prognosis within gigapixel whole-slide images. Experimental results demonstrate that LongViT effectively encodes gigapixel images and outperforms previous state-of-the-art methods on cancer subtyping and survival prediction. Code and models will be available at https://aka.ms/LongViT.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba

    cs.CV 2024-12 conditional novelty 6.0 of 10

    Pixel-Mamba, an end-to-end Mamba-based architecture with progressive token expansion, reports tumor staging and survival scores on three TCGA datasets that match or exceed several pathology foundation models without p...

  2. Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image

    cs.CV 2024-11 conditional novelty 6.0 of 10

    PathTree improves whole slide image classification by organizing disease categories as a binary tree and using expert-written pathological text to guide feature aggregation, achieving state-of-the-art results on three...

  3. Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images

    eess.IV 2025-06 conditional novelty 5.0 of 10

    HistoStainAlign uses contrastive alignment between paired H&E and IHC whole-slide embeddings to predict P53, PD-L1, and Ki-67 status from H&E slides alone, achieving moderate F1 scores on small internal datasets.

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