“…Secondly, the profuse heterogeneity across diverse endoscopic scenarios and equipment configurations imposes impediments on model generalization, necessitating a heightened degree of adaptability tailored to specific contexts. Thirdly, the exacting nature of medical applications necessitates outcomes that are endowed with an exquisite level of precision, thereby elevating the requisites for model performance and stability [42]. Lastly, the pursuit of interpretability and comprehensibility in the realm of automated detection presents a formidable challenge, as healthcare professionals seek to possess an all-encompassing grasp of the model's cognitive processes.…”
Section: Bounding Boxes In Clinical Endoscopymentioning
Endoscopy, a pervasive instrument for the diagnosis and treatment of hollow anatomical structures, conventionally necessitates the arduous manual scrutiny of seasoned medical experts. Nevertheless, the recent strides in deep learning technologies proffer novel avenues for research, endowing it with the potential for amplified robustness and precision, accompanied by the pledge of cost abatement in detection procedures, while simultaneously providing substantial assistance to clinical practitioners. Within this investigation, we usher in an innovative technique for the identification of anomalies in endoscopic imagery, christened as Context-enhanced Feature Fusion with Boundary-aware Convolution (GFFBAC). We employ the Context-enhanced Feature Fusion (CEFF) methodology, underpinned by Convolutional Neural Networks (CNNs), to establish equilibrium amidst the tiers of the feature pyramids. These intricately harnessed features are subsequently amalgamated into the Boundary-aware Convolution (BAC) module to reinforce both the faculties of localization and classification. A thorough exploration conducted across three disparate datasets elucidates that the proposition not only surpasses its contemporaries in object detection performance but also yields detection boxes of heightened precision.
“…Secondly, the profuse heterogeneity across diverse endoscopic scenarios and equipment configurations imposes impediments on model generalization, necessitating a heightened degree of adaptability tailored to specific contexts. Thirdly, the exacting nature of medical applications necessitates outcomes that are endowed with an exquisite level of precision, thereby elevating the requisites for model performance and stability [42]. Lastly, the pursuit of interpretability and comprehensibility in the realm of automated detection presents a formidable challenge, as healthcare professionals seek to possess an all-encompassing grasp of the model's cognitive processes.…”
Section: Bounding Boxes In Clinical Endoscopymentioning
Endoscopy, a pervasive instrument for the diagnosis and treatment of hollow anatomical structures, conventionally necessitates the arduous manual scrutiny of seasoned medical experts. Nevertheless, the recent strides in deep learning technologies proffer novel avenues for research, endowing it with the potential for amplified robustness and precision, accompanied by the pledge of cost abatement in detection procedures, while simultaneously providing substantial assistance to clinical practitioners. Within this investigation, we usher in an innovative technique for the identification of anomalies in endoscopic imagery, christened as Context-enhanced Feature Fusion with Boundary-aware Convolution (GFFBAC). We employ the Context-enhanced Feature Fusion (CEFF) methodology, underpinned by Convolutional Neural Networks (CNNs), to establish equilibrium amidst the tiers of the feature pyramids. These intricately harnessed features are subsequently amalgamated into the Boundary-aware Convolution (BAC) module to reinforce both the faculties of localization and classification. A thorough exploration conducted across three disparate datasets elucidates that the proposition not only surpasses its contemporaries in object detection performance but also yields detection boxes of heightened precision.
“… Computational issues and a scarcity of large-scale publicly available datasets Transformer-based visual language pre-trained MI zero-shot transfer 70.20 /–/ – HAG-MIL [ 69 ] Breast, cell, and lung TCGA (BRCA, NSCLC and RCC), etc. Computational issues and a scarcity of large-scale publicly available datasets Transformer-based visual language pre-trained MI zero-shot transfer 70.20 /–/ – MEGT [ 47 ] Kidney and breast TCGA-RCC and CAMELYON16 The problem of learning multi-scale image representation from large images like gigapixel WSIs Multi-scale efficient graph transformer-based network 96.91 / 96.26 / 97.30 MSPT [ 70 ] Breast, and lung TCGA-NSCLC and CAMELYON16 The problem of uneven representation between the negative and positive instances in bags Multi-scale prototypical transformer-based network 95.36 /–/ 98.69 GLAMIL [ 71 ] Breast, lung, and kidney TCGA(RCC and NSCLC) and CAMELYON16 Overfitting, WSI-level feature aggregation, and imbalanced data challenges Local-to-global spatial learning 95.01 /–/ 99.26 Fig. 9 Some examples of SOTA transformer architectures for histopathological image classification …”
Transformers have been widely used in many computer vision challenges and have shown the capability of producing better results than convolutional neural networks (CNNs). Taking advantage of capturing long-range contextual information and learning more complex relations in the image data, Transformers have been used and applied to histopathological image processing tasks. In this survey, we make an effort to present a thorough analysis of the uses of Transformers in histopathological image analysis, covering several topics, from the newly built Transformer models to unresolved challenges. To be more precise, we first begin by outlining the fundamental principles of the attention mechanism included in Transformer models and other key frameworks. Second, we analyze Transformer-based applications in the histopathological imaging domain and provide a thorough evaluation of more than 100 research publications across different downstream tasks to cover the most recent innovations, including survival analysis and prediction, segmentation, classification, detection, and representation. Within this survey work, we also compare the performance of CNN-based techniques to Transformers based on recently published papers, highlight major challenges, and provide interesting future research directions. Despite the outstanding performance of the Transformer-based architectures in a number of papers reviewed in this survey, we anticipate that further improvements and exploration of Transformers in the histopathological imaging domain are still required in the future. We hope that this survey paper will give readers in this field of study a thorough understanding of Transformer-based techniques in histopathological image analysis, and an up-to-date paper list summary will be provided at https://github.com/S-domain/Survey-Paper.
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