Trilateral Attention Network for Real-time Medical Image Segmentation

Zamzmi, Ghada; Sachdev, Vandana; Antani, Sameer

doi:10.48550/arxiv.2106.09201

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…The model classification performance will be enhanced by extracting the key traits distinguishing the different DR phases. Consequently, a wavelet scattering model [33] was used.…”

Section: Feature Extractionmentioning

confidence: 99%

An Enhancing Diabetic Retinopathy Classification and Segmentation based on TaNet

Vani,

Praneeth,

Kommareddy

et al. 2024

Nano Biomedicine and Engineering

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Human vision depends heavily on retinal tissue. The loss of eyesight may result from infections of the retinal tissues that are treated slowly or do not work at all. Additionally, the diagnosis is susceptible to inaccuracies when a large dataset is involved. Therefore, a fully automated transfer learning approach for diagnosing diabetic retinopathy (DR) is suggested to minimize human intervention while maintaining high classification accuracy. To address this issue, we proposed a transfer learning-based trilateral attention network (TaNet) for the classification. To boost the visual quality of the DR pictures, a contrast constrained adaptive histogram equalization approach is applied. The preprocessed pictures are then segmented using a bilateral segmentation network (BiSeNet). The BiSeNet segmented the optic disc and blood vessels individually. After the completion of segmentation, the features are extracted. Feature extraction is based on the wavelet scattering transformation approach. The results of many trials were evaluated against the Messidor-2, EYEPACS, and APTOS 2019 datasets. The proposed model was created using a refined pre-trained technique and transfer learning methodology. Finally, the suggested framework was tested using efficiency assessment methods, and the classification rate was recorded as having above 98% sensitivity, specificity, precision, and accuracy. The proposed approach yields greater performance and shows enhancement towards the existing approach.

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“…The model classification performance will be enhanced by extracting the key traits distinguishing the different DR phases. Consequently, a wavelet scattering model [33] was used.…”

Section: Feature Extractionmentioning

confidence: 99%

An Enhancing Diabetic Retinopathy Classification and Segmentation based on TaNet

Vani,

Praneeth,

Kommareddy

et al. 2024

Nano Biomedicine and Engineering

View full text Add to dashboard Cite

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“…The other is a semantic branch which has shallow layers but quick spatial pooling to aggregate information and context across a wide ROI over the original image. While this is quite applicable to our sUAS payload, it has also been utilized in various other problems, such as general robotic applications (Tzelepi and Tefas 2021) and pose measurement (Du et al 2021), and has inspired similar model architectures for the tasks of road segmentation (Bai, Lyu, and Huang 2021) and medical image segmentation (Zamzmi, Sachdev, and Antani 2021). Initial testing of this model shows a favorable performance profile when deployed against two state-of-theart edge devices, the NVIDIA Jetson Nano and Xavier, as shown in Table 2.…”

Section: Deep Neural Network Modelmentioning

confidence: 99%

ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI

Dávila¹,

VanPelt²,

Lynch³

et al. 2022

Preprint

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Small unmanned aircraft systems (sUAS) are becoming prominent components of many humanitarian assistance and disaster response (HADR) operations. Pairing sUAS with onboard artificial intelligence (AI) substantially extends their utility in covering larger areas with fewer support personnel. A variety of missions, such as search and rescue, assessing structural damage, and monitoring forest fires, floods, and chemical spills, can be supported simply by deploying the appropriate AI models. However, adoption by resourceconstrained groups, such as local municipalities, regulatory agencies, researchers, and indigenous persons, has been hampered by the lack of a cost-effective, readily-accessible baseline platform that can be easily adapted to their unique missions. To fill this gap, we have developed the fully free and open-source ADAPT multi-mission payload for deploying real-time AI and computer vision onboard a sUAS during local and beyond-line-of-site missions. We have emphasized a modular design with low-cost, readily-available components, open-source software, and thorough documentation (https: //kitware.github.io/adapt/). The system integrates an inertial navigation system, high-resolution color camera, computer, and wireless downlink to process imagery and broadcast georegistered analytics back to a ground station. Our goal is to make it easy for the HADR community to build their own copies of the ADAPT payload and leverage the thousands of hours of non-recurring engineering we have devoted to developing and testing this general-purpose capability. In this paper, we detail the development and testing of the ADAPT payload. We also demonstrate the example mission of real-time, in-flight ice segmentation to monitor river ice state and provide more-timely predictions of catastrophic flooding events. We deploy a novel active learning workflow to annotate river ice imagery, train a real-time deep neural network for ice segmentation, and demonstrate operation during field testing.

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“…1). Hence, considering the entire image for segmentation would add noise caused by irrelevant portions in the background and lead to the segmentation network being biased towards the background regions [4].…”

Section: Introductionmentioning

confidence: 99%

Region of Interest Identification in the Cervical Digital Histology Images

Biloborodova

Lomakin

Skarga-Bandurova

2022

Progress in Artificial Intelligence

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The region of interest (RoI) identification has a significant potential for yielding information about relevant histological features and is imperative to improve the effectiveness of digital pathology in clinical practice. The typical RoI is the stratified squamous epithelium (SSE) that appears on relatively small image areas. Hence, taking the entire image for classification adds noise caused by irrelevant background, making classification networks biased towards the background fragments. This paper proposes a novel approach for epithelium RoI identification based on automatic bounding boxes (bb) construction and SSE extraction and compares it with state-of-the-art histology RoI localization and detection techniques. Further classification of the extracted epithelial fragments based on DenseNet made it possible to effectively identify the SSE RoI in cervical histology images (CHI). The design brings significant improvement to the identification of diagnostically significant regions. For this research, we created two CHI datasets, the CHI-I containing 171 color images of the cervical histology microscopy and CHI-II containing 1049 extracted fragments of microscopy, which are the most considerable publicly available SSE datasets.

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Trilateral Attention Network for Real-time Medical Image Segmentation

Cited by 3 publications

References 30 publications

An Enhancing Diabetic Retinopathy Classification and Segmentation based on TaNet

An Enhancing Diabetic Retinopathy Classification and Segmentation based on TaNet

ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI

Region of Interest Identification in the Cervical Digital Histology Images

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