Sorry I can’t find the full paper only the abstract
Neuromorphic Medical Image Analysis at the Edge: On-Edge training with the Akida Brainchip
DiVA portal is a finding tool for research publications and student theses written at the following 50 universities and research institutions.www.diva-portal.org
Neuromorphic Medical Image Analysis at the Edge: On-Edge training with the Akida Brainchip
Bråtman, Ebba
KTH, School of Electrical Engineering and Computer Science (EECS).
Dow, Lucas
KTH, School of Electrical Engineering and Computer Science (EECS).
2023 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Medicinsk bildanalys med neuromorfisk hårdvara : On-Edge-träning med Akida Brainchip (Swedish)
Abstract [en]
Computed Tomography (CT) scans play a crucial role in medical imaging, allowing neuroscientists to identify intracranial pathologies such as haemorrhages and malignant tumours in the brain. This thesis explores the potential of deep learning models as an aid in intracranial pathology detection through medical imaging. By first creating a convolutional neural network model capable of identifying brain haemorrhage and then moving it onto the neuromorphic processor Akida AKD1000, it allowed the usage of Spiking Neural Networks and on-edge retraining capabilities. In a process called few-shot learning, the model was trained to also identify brain tumours with minimal additional samples. The research further investigated how the parameters used in the edge-learning influenced classification accuracy. It was shown that the parameter selection and interaction introduced a trade-off in regard to accuracy for the haemorrhage and tumour classification models, but an optimal constellation of parameters could be extracted. These results aim to serve as a foundation for future endeavours in image analysis using neuromorphic hardware, specifically within the domain of few-shot and on-edge learning. The integration of these models in the medical field has the potential to streamline the diagnosis of intracranial pathologies, enhancing accuracy and efficiency while unloading medical professionals.
There we go again....AKD 1000 doing us so proud !! "Too narrow in it's offerings" that comment still haunts me today.
I fully understand how new, more advanced iterations of Akida must be developed to drive our company forward in doing so, accommodating current and future customers requirements as we all grow, but as I have commented on many times now, please appreciate how AKD1000 was
a masterly achievement a few years ago.
As far as newer iterations are concerned, I think there is a little bit of confusion with regards AKD I AKD II AKD III up to AKD X...I did ask
Peter about this very thing in 2022/23 as to how he saw future iterations evolving over the next 7 year period, i.e. 2023 thru to 2030.
With regards reaching AGI by or around 2030, he said that it's possible in his opinion, but nothing is 100% certain, also that following the
earlier suggested path that appeared in an old slide showing AKD 1 1.5 thru to AKD X wasn't the plan in his mind, but I'll track down his
emailed reply to me before going any further, just in case I mix up his words and find myself in a spot of trouble, so to speak.
I'm hoping to hear some news late in 2024, early 2025 as to how AKD III is or has progressed, but lets not get too far ahead of ourselves,
we first need to achieve some solid traction reflected by ever increasing revenue streams created by both AKD 1 and AKD II....also it
would be nice to hear from our new guy in charge of sales, Steve Thorne on a company podcast, possibly too early but something during
the year would be nice in my opinion.
Regards to all.....Tech