New research aims to open the ‘black box’ of computer vision
It can take years of birdwatching experience to tell one species from the next. But using an artificial intelligence technique called deep learning, Duke University researchers have trained a computer to identify up to 200 species of birds from just a photo.
The real innovation, however, is that the A.I. tool also shows its thinking, in a way that even someone who doesn’t know a penguin from a puffin can understand.
The team trained their deep neural network — algorithms based on the way the brain works — by feeding it 11,788 photos of 200 bird species to learn from, ranging from swimming ducks to hovering hummingbirds.
The researchers never told the network “this is a beak” or “these are wing feathers.” Given a photo of a mystery bird, the network is able to pick out important patterns in the image and hazard a guess by comparing those patterns to typical species traits it has seen before.
Along the way it spits out a series of heat maps that essentially say: “This isn’t just any warbler. It’s a hooded warbler, and here are the features — like its masked head and yellow belly — that give it away.”
Duke computer science Ph.D. student Chaofan Chen and undergraduate Oscar Li led the research, along with other team members of the Prediction Analysis Lab directed by Duke professor Cynthia Rudin.
They found their neural network is able to identify the correct species up to 84% of the time — on par with some of its best-performing counterparts, which don’t reveal how they are able to tell, say, one sparrow from the next.
Rudin says their project is about more than naming birds. It’s about visualizing what deep neural networks are really seeing when they look at an image.
Similar technology is used to tag people on social networking sites, spot suspected criminals in surveillance cameras, and train self-driving cars to detect things like traffic lights and pedestrians.
The problem, Rudin says, is that most deep learning approaches to computer vision are notoriously opaque. Unlike traditional software, deep learning software learns from the data without being explicitly programmed. As a result, exactly how these algorithms ‘think’ when they classify an image isn’t always clear.
Rudin and her colleagues are trying to show that A.I. doesn’t have to be that way. She and her lab are designing deep learning models that explain the reasoning behind their predictions, making it clear exactly why and how they came up with their answers. When such a model makes a mistake, its built-in transparency makes it possible to see why.
For their next project, Rudin and her team are using their algorithm to classify suspicious areas in medical images like mammograms. If it works, their system won’t just help doctors detect lumps, calcifications and other symptoms that could be signs of breast cancer. It will also show which parts of the mammogram it’s homing in on, revealing which specific features most resemble the cancerous lesions it has seen before in other patients.
In that way, Rudin says, their network is designed to mimic the way doctors make a diagnosis. “It’s case-based reasoning,” Rudin said. “We’re hoping we can better explain to physicians or patients why their image was classified by the network as either malignant or benign.”
Learn more: THIS A.I. BIRDWATCHER LETS YOU ‘SEE’ THROUGH THE EYES OF A MACHINE
The Latest on: Deep learning
[google_news title=”” keyword=”deep learning” num_posts=”10″ blurb_length=”0″ show_thumb=”left”]
via Google News
The Latest on: Deep learning
- ARVO 2024: How a deep learning model can benefit femtosecond laser-assisted cataract surgeryon May 7, 2024 at 1:02 pm
Dustin Morley, PhD, principal research scientist at LENSAR, discusses research on applying deep learning to benefit FLACS procedures.
- Carnegie Mellon develops deep-learning alternative to in-situ PBF-LB monitoringon May 7, 2024 at 5:26 am
Researchers have developed a deep-learning approach to capture melt pools in PBF-LB Additive Manufacturing using airborne or thermal emissions ...
- AI Deep Learning Improves Brain-Computer Interface Performanceon May 6, 2024 at 8:54 am
AI deep learning powers a brain-computer interface that enables humans to continuously control a cursor using thoughts.
- RoadMap Technologies Releases RoadMap TrailBlazer™ Cloud-Based Deep Learning Software for Time Series Forecastingon May 6, 2024 at 5:00 am
RoadMap Technologies, a leading provider of data science and software solutions for the Life Sciences industry, today announced the release of RoadMap TrailBlazer™ Cloud-based forecasting software ...
- ARVO 2024: Deep learning model for GA segmentation, adaptable to SS-OCT and SD-OCT dataon May 5, 2024 at 3:38 pm
At this year's ARVO meeting, Qinqin Zhang, PhD, presented a poster titled "A unified deep learning model for geographic atrophy segmentation: Adaptable to SS-OCT and SD-OCT data with multiple scan ...
- Researchers develop deep-learning model for streamflow, flood forecastingon May 5, 2024 at 9:27 am
Chinese researchers have proposed a novel hybrid deep-learning model to address streamflow forecasting for water catchment areas at a global scale wi ...
- Deep learning techniques for Hyperspectral image analysis in agricultureon May 5, 2024 at 5:20 am
These innovations underline the industry’s brisk evolution, paving the way for hyperspectral imaging (HSI) and deep learning to further revolutionize farming practices, enhancing efficiency and ...
- New multi-task deep learning framework integrates large-scale single-cell proteomics and transcriptomics dataon April 26, 2024 at 7:35 am
The exponential progress in single-cell multi-omics technologies has led to the accumulation of large and diverse multi-omics datasets. However, the integration of single-cell proteomics and ...
- Europe taps deep learning to make industrial robots safer colleagueson April 26, 2024 at 1:07 am
European researchers have launched the RoboSAPIENS project to make adaptive industrial robots more efficient and safer to work with humans.
- Using deep learning to image the Earth's planetary boundary layeron April 17, 2024 at 5:00 pm
While this approach has improved observations of the atmosphere down to the surface overall, including the PBL, laboratory staff determined that newer "deep" learning techniques that treat the ...
via Bing News