Learning feature fusion via an interpretation method for tumor segmentation on PET/CT

Kang, Susu; Chen, Zhiyuan; Li, Laquan; Lu, Wei; Qi, X. Sharon; Tan, Shan

doi:10.1016/j.asoc.2023.110825

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…This section discusses the results of comparative experiments using five representative deep learning models that are among the best in terms of generalisation and precision in the medical image segmentation field. These models include nnU-Net [6], known for its excellent generalisability and accuracy; SSL-ALPNet [7], tailored for abdominal CT segmentation tasks with small datasets; HRSTNet [8], an abdominal CT segmentation network based on the swin-transformer; FFI [38], which uses dual-modalities for contrastive learning; and UNesT [39], a semi-supervised learning model that combines convolutional neural networks with transformers.…”

Section: Comparison With Deep Learning-based Methodsmentioning

confidence: 99%

RFLSE: Joint radiomics feature‐enhanced level‐set segmentation for low‐contrast SPECT/CT tumour images

Guo,

Qin,

Zeng

et al. 2024

IET Image Processing

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Doctors typically use non‐contrast‐enhanced computed tomography (NCECT) in the treatment of kidney cancer to map kidney and tumour structural information to functional imaging single‐photon emission computed tomography, which is then used to assess patient kidney function and predict postoperative recovery. However, the assessment of kidney function and formulation of surgical plans is constrained by the low contrast of tumours in NCECT, which hinders the acquisition of accurate tumour boundaries. Therefore, this study designed a radiomics feature‐enhanced level‐set evolution (RFLSE) to precisely segment small‐sample low‐contrast kidney tumours. Integration of high‐dimensional radiomics features into the level‐set energy function enhances the edge detection capability of low‐contrast kidney tumours. The use of sensitive radiomics features to control the regional term parameters achieves adaptive adjustment of the curve evolution amplitude, improving the level‐set segmentation process. The experimental data used low‐contrast, limited‐sample tumours provided by hospitals, as well as the public datasets BUSI18 and KiTS19. Comparative results with advanced energy functionals and deep learning models demonstrate the precision and robustness of RFLSE segmentation. Additionally, the application value of RFLSE in assisting doctors with accurately marking tumours and generating high‐quality pseudo‐labels for deep learning datasets is demonstrated.

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Section: Comparison With Deep Learning-based Methodsmentioning

confidence: 99%

RFLSE: Joint radiomics feature‐enhanced level‐set segmentation for low‐contrast SPECT/CT tumour images

Guo,

Qin,

Zeng

et al. 2024

IET Image Processing

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Explainable AI (XAI) in image segmentation in medicine, industry, and beyond: A survey

Gipiškis,

Tsai,

Kurasova

2024

ICT Express

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Histopathology in focus: a review on explainable multi-modal approaches for breast cancer diagnosis

Abdullakutty,

Akbari,

Al-Maadeed

et al. 2024

Front. Med.

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Precision and timeliness in breast cancer detection are paramount for improving patient outcomes. Traditional diagnostic methods have predominantly relied on unimodal approaches, but recent advancements in medical data analytics have enabled the integration of diverse data sources beyond conventional imaging techniques. This review critically examines the transformative potential of integrating histopathology images with genomic data, clinical records, and patient histories to enhance diagnostic accuracy and comprehensiveness in multi-modal diagnostic techniques. It explores early, intermediate, and late fusion methods, as well as advanced deep multimodal fusion techniques, including encoder-decoder architectures, attention-based mechanisms, and graph neural networks. An overview of recent advancements in multimodal tasks such as Visual Question Answering (VQA), report generation, semantic segmentation, and cross-modal retrieval is provided, highlighting the utilization of generative AI and visual language models. Additionally, the review delves into the role of Explainable Artificial Intelligence (XAI) in elucidating the decision-making processes of sophisticated diagnostic algorithms, emphasizing the critical need for transparency and interpretability. By showcasing the importance of explainability, we demonstrate how XAI methods, including Grad-CAM, SHAP, LIME, trainable attention, and image captioning, enhance diagnostic precision, strengthen clinician confidence, and foster patient engagement. The review also discusses the latest XAI developments, such as X-VARs, LeGrad, LangXAI, LVLM-Interpret, and ex-ILP, to demonstrate their potential utility in multimodal breast cancer detection, while identifying key research gaps and proposing future directions for advancing the field.

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Learning feature fusion via an interpretation method for tumor segmentation on PET/CT

Cited by 3 publications

References 25 publications

RFLSE: Joint radiomics feature‐enhanced level‐set segmentation for low‐contrast SPECT/CT tumour images

RFLSE: Joint radiomics feature‐enhanced level‐set segmentation for low‐contrast SPECT/CT tumour images

Explainable AI (XAI) in image segmentation in medicine, industry, and beyond: A survey

Histopathology in focus: a review on explainable multi-modal approaches for breast cancer diagnosis

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