PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnostics

Daniel, Nati; Larey, Ariel; Aknin, Eliel; Osswald, G. A.; Caldwell, Julie M.; Rochman, Mark; Collins, Margaret H.; Yang, Guangyu; Arva, Nicoleta C.; Capocelli, Kelley E.; Rothenberg, Marc E.; Savir, Yonatan

doi:10.48550/arxiv.2103.02015

Cited by 3 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another group of scientists used a smaller dataset including 4345 images from 23 patients with EoE, where a trained pathologist labeled each pixel as including an intact eosinophil, non-intact eosinophil, or non-eosinophil. Using a U-net model, they could predict EoE diagnosis with 95% accuracy [18]. Differentiating intact vs. non-intact eosinophils (which are not included in PEC) can be challenging for pathologists, and this model correctly identified 98.8% of intact eosinophils.…”

Section: Ai For Biopsy Analysismentioning

confidence: 99%

Applications of Artificial Intelligence to Eosinophilic Esophagitis

Smith

Shah

2022

Gastroenterology Insights

View full text Add to dashboard Cite

Eosinophilic Esophagitis (EoE) is a chronic immune-related inflammation, and challenges to its diagnosis and treatment evaluation persist. This literature review evaluates all AI applications to EOE, including 15 studies using AI algorithms for counting eosinophils in biopsies, as well as newer diagnostics using mRNA transcripts in biopsies, endoscopic photos, blood and urine biomarkers, and an improved scoring system for disease classification. We also discuss the clinical impact of these models, challenges faced in applying AI to EoE, and future applications. In conclusion, AI has the potential to improve diagnostics and clinical evaluation in EoE, improving patient outcomes.

show abstract

Section: Ai For Biopsy Analysismentioning

confidence: 99%

Applications of Artificial Intelligence to Eosinophilic Esophagitis

Smith

Shah

2022

Gastroenterology Insights

View full text Add to dashboard Cite

show abstract

“…Human trajectory forecasting in crowds has been an active area of research [7], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29] for various applications like autonomous systems [30], [31], [32], [33] and advanced surveillance [34]. In this section, we review model designs that learn social interactions and output socially compliant multimodal outputs.…”

Section: Related Workmentioning

confidence: 99%

“…Further, the value of k is very high (k = 20 being most common). Almost all the recent works [2], [3], [14], [17], [24] in human trajectory forecasting utilize the Top-20 ADE/FDE metric [2] to quantify multimodal performance. We argue that measuring multimodal performance based solely on this metric can be misleading.…”

Section: B Limitations Of Current Multimodal Evaluation Schemementioning

confidence: 99%

“…Our Top-3 ADE/FDE are on par with (if not better than) state-of-theart methods while our collision rate is significantly reduced thanks to spatio-temporal interaction modelling. It is further interesting to note that Trajectory Transformer [17] and the [23], [24], [43]. SGANv2 performs at par with the top-ranked PECNet [24] on the Top-3 evaluation while having 3x lower collisions demonstrating that spatio-temporal interaction modelling is key to outputting safer trajectories.…”

Section: E Real-world Experimentsmentioning

confidence: 99%

“…It is further interesting to note that Trajectory Transformer [17] and the [23], [24], [43]. SGANv2 performs at par with the top-ranked PECNet [24] on the Top-3 evaluation while having 3x lower collisions demonstrating that spatio-temporal interaction modelling is key to outputting safer trajectories. 1 Additionally, we utilize the open-source implementation of three additional state-of-the-art methods (denoted by †) and evaluate them on the TrajNet++ benchmark.…”

Section: E Real-world Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

Safety-Compliant Generative Adversarial Networks for Human Trajectory Forecasting

Kothari

Alahi

2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Human trajectory forecasting in crowds presents the challenges of modelling social interactions and outputting collision-free multimodal distribution. Following the success of Social Generative Adversarial Networks (SGAN), recent works propose various GAN-based designs to better model human motion in crowds. Despite superior performance in reducing distance-based metrics, current networks fail to output socially acceptable trajectories, as evidenced by high collisions in model predictions. To counter this, we introduce SGANv2: an improved safety-compliant SGAN architecture equipped with spatio-temporal interaction modelling and a transformer-based discriminator. The spatio-temporal modelling ability helps to learn the human social interactions better while the transformer-based discriminator design improves temporal sequence modelling. Additionally, SGANv2 utilizes the learned discriminator even at test-time via a collaborative sampling strategy that not only refines the colliding trajectories but also prevents mode collapse, a common phenomenon in GAN training. Through extensive experimentation on multiple real-world and synthetic datasets, we demonstrate the efficacy of SGANv2 to provide socially-compliant multimodal trajectories.

show abstract

Eosinophils instance object segmentation on whole slide imaging using multi-label circle representation

Liu,

Deng,

Xiong

et al. 2024

Medical Imaging 2024: Digital and Computational Pathology

View full text Add to dashboard Cite

Eosinophilic esophagitis (EoE) is a chronic and relapsing disease characterized by esophageal inflammation. Symptoms of EoE include difficulty swallowing, food impaction, and chest pain which significantly impact the quality of life, resulting in nutritional impairments, social limitations, and psychological distress. The diagnosis of EoE is typically performed with a threshold (15 to 20) of eosinophils (Eos) per high-power field (HPF). Since the current counting process of Eos is a resource-intensive process for human pathologists, automatic methods are desired. Circle representation has been shown as a more precise, yet less complicated, representation for automatic instance cell segmentation such as CircleSnake approach. However, the CircleSnake was designed as a single-label model, which is not able to deal with multi-label scenarios. In this paper, we propose the multi-label CircleSnake model for instance segmentation on Eos. It extends the original CircleSnake model from a single-label design to a multi-label model, allowing segmentation of multiple object types. Experimental results illustrate the CircleSnake model's superiority over the traditional Mask R-CNN model and DeepSnake model in terms of average precision (AP) in identifying and segmenting eosinophils, thereby enabling enhanced characterization of EoE. This automated approach holds promise for streamlining the assessment process and improving diagnostic accuracy in EoE analysis. The source code has been made publicly available at https://github.com/yilinliu610730/ EoE.

show abstract

PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnostics

Cited by 3 publications

References 0 publications

Applications of Artificial Intelligence to Eosinophilic Esophagitis

Applications of Artificial Intelligence to Eosinophilic Esophagitis

Safety-Compliant Generative Adversarial Networks for Human Trajectory Forecasting

Eosinophils instance object segmentation on whole slide imaging using multi-label circle representation

Contact Info

Product

Resources

About