Multi-scale Deformable Transformer for the Classification of Gastric Glands: The IMGL Dataset

Abstract

Gastric cancer is one of the most common cancers and a leading cause of cancer-related death worldwide. Among the risk factors of gastric cancer, the gastric intestinal metaplasia (IM) has been found to increase the risk of gastric cancer and is considered as one of the precancerous lesions. Therefore, early detection of IM could allow risk stratification regarding the possibility of progression to cancer. To this end, accurate classification of gastric glands from the histological images plays an important role in the diagnostic confirmation of IM. To date, although many gland segmentation approaches have been proposed, no general model has been proposed for the identification of IM glands. Thus, in this paper, we propose a model for gastric glands' classification. More specifically, we propose a multi-scale deformable transformer-based network for glands' classification into normal and IM gastric glands. To evaluate the efficiency of the proposed methodology we created the IMGL dataset consisting of 1000 gland images, including both intestinal metaplasia and normal cases received from 20 Whole Slide Images (WSI). The results showed that the proposed approach achieves an F1 score equal to 0.94, showing great potential for the gastric glands' classification.

A survey of Transformer applications for histopathological image analysis: New developments and future directions

“…Figure 9 shows some examples of SOTA transformer architectures developed for histopathological image classification. DT-DSMIL [56]), gastric cancer classification (IMGL-VTNet [57]), kidney subtype classification (i-ViT [59], tRNAsformer [58]), thymoma or thymic carcinoma classification (MC-ViT [76]), lung cancer classification (GTP [46], FDTrans [60]), skin cancer classification (Wang et al [45]), and thyroid cancer classification (Wang et al [77], PyT2T-ViT [41], Wang et al [78]) using different transformer-based architectures. Furthermore, other transformer models such as Transmil [65], KAT [61], ViT-based unsupervised contrastive learning architecture [79], DecT [66], StoHisNet [80], CWC-transformer [63], LA-MIL [44], SETMIL [81], Prompt-MIL [67], GLAMIL [67], MaskHIT [82], HAG-MIL [68], MEGT [47], MSPT [70], and HistPathGPT [69] have also been evaluated on more than one tissue type, such as liver, prostate, breast, brain, gastric, kidney, lung, colorectal, and so on, for histopathological image classification using different transformer approaches.…”

Suburban Forest Fire Risk Assessment and Forest Surveillance Using 360-Degree Cameras and a Multiscale Deformable Transformer

“…By leveraging the strengths of both deep learning and multidimensional texture analysis, the authors aimed to achieve improved results in early fire detection. More recently, vision transformers [18][19][20][21][22] inspired by the deep learning model that was developed for natural language processing [23] have been employed for various applications as well as fire detection and classification of fire. More specifically, attention layers have been utilized in different ways by vision transformers.…”

“…More specifically, attention layers have been utilized in different ways by vision transformers. For example, Barmpoutis et al [20] investigated the use of a spatial and multiscale feature enhancement module; Xu et al [21] designed a fused axial attention module capturing local and global spatial interactions; and Tu et al [22] introduced a multiaxis attention model which utilizes global-local spatial interactions. Focusing on fire detection, Ghali et al [24] used two vision-based transformers, namely, TransUNet and MedT, extracting both global and local features in order to reduce fire-pixel misclassifications.…”