【aetherAI】AI-Assisted Diagnosis of Lymph Node Metastasis using GigaPixel AI technology

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The 20th annual meeting of Japanese Society of Digital Pathology

Health & Medicine

AI-Assisted Diagnosis of
Lymph Node Metastasis
using GigaPixel AI technology
執行長葉肇元醫師
CEO, Joe Yeh, M.D.

Pitfalls of Traditional Diagnostic Workflow
for Lymph Node Metastasis
1. Repetitive & tedious
2. Time-consuming: need to count
nodes and write report
3. Risk of missing out micro-
metastases in difficult case
JAMA. 2017;318(22):2199-
2210
High probability of missing out micro-metastases
2

Giga-pixel challenge of WSIs
Chest X-ray
2,000 × 2,500 (5MP) 40× Whole-slide Image (WSI)
200,000 × 100,000 (20GP)
4

Common approach: patch-based method
• Patch-based method requires extensive detailed annotations by
human experts (1 hour per slide)
Cancer (P)
Benign (N)
5

The Power of Gigapixel AI:
Training AI to recognize metastasis using only
lymph node-level annotation
6

Deep neural network trained on ~6000 gigapixel lymph node
images exhibit superior ability in recognizing micrometastasis
Micrometastasis vs Negative Lymph Nodes (n=919)
7

Examples of lymph node metastasis detected by AI
8

Clinical impact evaluation: Better sensitivity
• The workflow significantly improved the sensitivity of micrometastasis
identification (81.94% to 95.83%, P < .001) and isolated tumor cells
(67.95% to 96.15%, P < .001)
9

Clinical impact evaluation: Shorter review time
• Per-slide median: 161.2 to 110.5 s, −31.5%, P < .001
10

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Clinical imaging is playing a fundamental limit in assessment and patching of affliction and discovering tumors and finding of threatening cells in less than ideal stage. As a standard system for perceiving bone features, is minute pictures were used. These photos are secured by using small radiography, where it expected to reiterated, drawn out and work raised measure. This method cant recognize the destructive cells because of the presence of uproar in the photos. Hence there is a necessity for automated and strong strategies to finish the image planning examination. As a first stage, the most fundamental piece of picture planning is to denoising without barging in on the diagnostics information during the clearing of commotion. The past collaboration disposes of the uproar and present fog in the image. To get precise picture getting ready, we have executed fragile and hard breaking point with various coefficients and to check the edge Visu wither was used. It was found that the Wavelet deionsing gadget was a helpful resource for picture improvement. In the gathering, our proposed work was connected with pre planning methodology to wipe out the noise and to get smooth pictures. This collaboration will help with improving the idea of the image and besides take out the fake areas. To recognize the presence of bone illness and to choose its stage, K infers estimation was used and thusly to get smooth picture, edge division measure was performed. Miss. Kode Keerthi | Mr. Parasurama N "Implementation of Medical Image Analysis using Image Processing Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39893.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39893/implementation-of-medical-image-analysis-using-image-processing-techniques/miss-kode-keerthi

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