Name
Title | ||
|---|---|---|
Instance-Based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image |
Abstract | ||
|---|---|---|
Histological subtype of papillary (p) renal cellular and cell-layer level patterns almost cannot be captured by existing CNN-based models in large-size histopathological images, which brings obstacles to directly applying these models to such a fine-grained classification task. This paper proposes a novel instance-based Vision Transformer (i-ViT) to learn robust representations of histopathological images for the pRCC subtyping task by extracting finer features from instance patches (by cropping around segmented nuclei and assigning predicted grades). The proposed i-ViT takes top-K instances as the inputs and aggregates them for capturing both the cellular and cell-layer level patterns by a position-embedding layer, a grade-embedding layer, and a multi-head multi-layer self-attention module. To evaluate the performance of the proposed framework, experienced pathologists select 1162 regions of interest from 171 whole slide images of type 1 and type 2 pRCC. Experimental results show that the proposed method achieves better performance than existing CNN-based models with a significant margin. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-87237-3_29 | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VIII |
Keywords | DocType | Volume |
Fine-grained classification, Transformer, Histopathology | Conference | 12908 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.37 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeyu Gao | 1 | 1 | 0.71 |
| Bangyang Hong | 2 | 1 | 0.37 |
| Xianli Zhang | 3 | 9 | 4.61 |
| Yang Li | 4 | 2 | 2.43 |
| Chang Jia | 5 | 1 | 0.37 |
| Jialun Wu | 6 | 1 | 3.08 |
| Chunbao Wang | 7 | 1 | 3.75 |
| Deyu Meng | 8 | 2025 | 105.31 |
| Chen Li | 9 | 80 | 54.64 |