Search Results for author:
Found 122 papers, 44 papers with code
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement
Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality.
Ranked #5 on
Speech Enhancement
on Deep Noise Suppression (DNS) Challenge
(PESQ-NB metric)
Speech Enhancement
Audio and Speech Processing
Sound
Robust and Efficient Medical Imaging with Self-Supervision
These results suggest that REMEDIS can significantly accelerate the life-cycle of medical imaging AI development thereby presenting an important step forward for medical imaging AI to deliver broad impact.
Richer Convolutional Features for Edge Detection
Using VGG16 network, we achieve \sArt results on several available datasets.
Ranked #5 on
Edge Detection
on BIPED
Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform
Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources.
Ranked #2 on
Real-Time 3D Semantic Segmentation
on SemanticKITTI
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
The use of RGB-D information for salient object detection has been extensively explored in recent years.
Ranked #4 on
RGB-D Salient Object Detection
on RGBD135
P2T: Pyramid Pooling Transformer for Scene Understanding
A popular solution to this problem is to use a single pooling operation to reduce the sequence length.
Ranked #5 on
RGB Salient Object Detection
on DUTS-TE
(max F-measure metric)
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation
Despite the capabilities of these approaches in capturing temporal dependencies, their predictions suffer from over-segmentation errors.
Ranked #5 on
Action Segmentation
on Assembly101
LEVEN: A Large-Scale Chinese Legal Event Detection Dataset
However, existing Legal Event Detection (LED) datasets only concern incomprehensive event types and have limited annotated data, which restricts the development of LED methods and their downstream applications.
Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots
Our framework features a two-stream encoder, an active fusion decoder (AFD) and a dual-task regularization approach.
Uformer: A Unet based dilated complex & real dual-path conformer network for simultaneous speech enhancement and dereverberation
Complex spectrum and magnitude are considered as two major features of speech enhancement and dereverberation.
Vision Transformers with Hierarchical Attention
This paper tackles the high computational/space complexity associated with Multi-Head Self-Attention (MHSA) in vanilla vision transformers.
SnowFormer: Context Interaction Transformer with Scale-awareness for Single Image Desnowing
Due to various and complicated snow degradations, single image desnowing is a challenging image restoration task.
Learning Local and Global Temporal Contexts for Video Semantic Segmentation
Specifically, we uniformly sample certain frames from the video and extract global contextual prototypes by k-means.
Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution
Transformer-based methods have exhibited remarkable potential in single image super-resolution (SISR) by effectively extracting long-range dependencies.
Ranked #26 on
Image Super-Resolution
on Set14 - 4x upscaling
Simplified Transfer Learning for Chest Radiography Models Using Less Data
Supervised contrastive learning enabled performance comparable to state-of-the-art deep learning models in multiple clinical tasks by using as few as 45 images and is a promising method for predictive modeling with use of small data sets and for predicting outcomes in shifting patient populations.
Structure-measure: A New Way to Evaluate Foreground Maps
Our new measure simultaneously evaluates region-aware and object-aware structural similarity between a SM and a GT map.
Crowd Counting With Deep Negative Correlation Learning
Deep convolutional networks (ConvNets) have achieved unprecedented performances on many computer vision tasks.
Ranked #9 on
Crowd Counting
on WorldExpo’10
Indiscernible Object Counting in Underwater Scenes
We further advance the frontier of this field by systematically studying a new challenge named indiscernible object counting (IOC), the goal of which is to count objects that are blended with respect to their surroundings.
Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement
In this work, we propose the Five A$^{+}$ Network (FA$^{+}$Net), a highly efficient and lightweight real-time underwater image enhancement network with only $\sim$ 9k parameters and $\sim$ 0. 01s processing time.
Regularized Densely-connected Pyramid Network for Salient Instance Segmentation
Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels.
Underwater Light Field Retention : Neural Rendering for Underwater Imaging
To this end, we propose a neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural Rendering).
Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation
For each proposal, this MIL framework can simultaneously compute probability distributions and category-aware semantic features, with which we can formulate a large undirected graph.
Image-level Supervised Instance Segmentation
Multiple Instance Learning
+3
EDN: Salient Object Detection via Extremely-Downsampled Network
In this paper, we tap into this gap and show that enhancing high- level features is essential for SOD as well.
HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction
We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the research of category-level human-object interaction.
Generalized Zero-Shot Learning via VAE-Conditioned Generative Flow
Recent generative methods formulate GZSL as a missing data problem, which mainly adopts GANs or VAEs to generate visual features for unseen classes.
Mining Relations among Cross-Frame Affinities for Video Semantic Segmentation
The essence of video semantic segmentation (VSS) is how to leverage temporal information for prediction.
Rethinking Few-shot 3D Point Cloud Semantic Segmentation
The former arises from non-uniform point sampling, allowing models to distinguish the density disparities between foreground and background for easier segmentation.
Few-shot 3D Point Cloud Semantic Segmentation
Segmentation
+1
MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation
On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive.
NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer
Extensive experiments on several synthetic and real-world datasets demonstrate the superiority of our NightHazeFormer over state-of-the-art nighttime haze removal methods in terms of both visually and quantitatively.
Semantic Edge Detection with Diverse Deep Supervision
Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition.
Scoot: A Perceptual Metric for Facial Sketches
In this paper, we design a perceptual metric, called Structure Co-Occurrence Texture (Scoot), which simultaneously considers the block-level spatial structure and co-occurrence texture statistics.
Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net
The asymmetric bilateral encoder has a transformer path and a lightweight CNN path, where the two paths communicate at each encoder stage to learn complementary global contexts and local spatial details, respectively.
Detecting Cancer Metastases on Gigapixel Pathology Images
At 8 false positives per image, we detect 92. 4% of the tumors, relative to 82. 7% by the previous best automated approach.
Ranked #2 on
Medical Object Detection
on Barrett’s Esophagus
Rethinking 3D LiDAR Point Cloud Segmentation
Many point-based semantic segmentation methods have been designed for indoor scenarios, but they struggle if they are applied to point clouds that are captured by a LiDAR sensor in an outdoor environment.
Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing
When manipulating an object to accomplish complex tasks, humans rely on both vision and touch to keep track of the object's 6D pose.
DNA: Deeply-supervised Nonlinear Aggregation for Salient Object Detection
Recent progress on salient object detection mainly aims at exploiting how to effectively integrate multi-scale convolutional features in convolutional neural networks (CNNs).
MobileSal: Extremely Efficient RGB-D Salient Object Detection
Therefore, we propose an implicit depth restoration (IDR) technique to strengthen the mobile networks' feature representation capability for RGB-D SOD.
Jointly Learning Propagating Features on the Knowledge Graph for Movie Recommendation
In the proposed framework, we use an attention-based multi-hop propagation mechanism to take users and movies as center nodes and extend their attributes along with the connections of the knowledge graph by recursively calculating the different contributions of their neighbors.
Revisiting Computer-Aided Tuberculosis Diagnosis
This dataset enables the training of sophisticated detectors for high-quality CTD.
Efficacious symmetry-adapted atomic displacement method for lattice dynamical studies
Small displacement methods have been successfully used to calculate the lattice dynamical properties of crystals.
Materials Science
Knowledge-aware attentional neural network for review-based movie recommendation with explanations
In this paper, we propose a knowledge-aware attentional neural network (KANN) for dealing with movie recommendation tasks by extracting knowledge entities from movie reviews and capturing understandable interactions between users and movies at the knowledge level.
Zero Pixel Directional Boundary by Vector Transform
One of the key problems in boundary detection is the label representation, which typically leads to class imbalance and, as a consequence, to thick boundaries that require non-differential post-processing steps to be thinned.
Masked Diffusion with Task-awareness for Procedure Planning in Instructional Videos
The introduced mask acts akin to a task-oriented attention filter, enabling the diffusion/denoising process to concentrate on a subset of action types.
Image Matching: An Application-oriented Benchmark
To this end, we present a uniform benchmark with novel evaluation metrics and a large-scale dataset for evaluating the overall performance of image matching methods.
Learning Pixel-wise Labeling from the Internet without Human Interaction
Collecting sufficient annotated data is very expensive in many applications, especially for pixel-level prediction tasks such as semantic segmentation.
Personalized Recommender System for Children's Book Recommendation with A Realtime Interactive Robot
In this paper we study the personalized book recommender system in a child-robot interactive environment.
Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning
Traditionally, medical discoveries are made by observing associations and then designing experiments to test these hypotheses.
Sequential Optimization for Efficient High-Quality Object Proposal Generation
We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING but significantly improves its proposal localization quality.
Transferring Knowledge from Text to Predict Disease Onset
Specifically, we use word2vec models trained on a domain-specific corpus to estimate the relevance of each feature's text description to the prediction problem.
MatchBench: An Evaluation of Feature Matchers
This leads to a critical absence in this field that there is no standard datasets and evaluation metrics to evaluate different feature matchers fairly.
Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer
For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage.
Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration
We demonstrate the utility of ARM in the detection of lymph node metastases in breast cancer and the identification of prostate cancer with a latency that supports real-time workflows.
Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photos
An algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers.
Salient Object Detection via High-to-Low Hierarchical Context Aggregation
In this paper, we observe that the contexts of a natural image can be well expressed by a high-to-low self-learning of side-output convolutional features.
Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection
OOF is often only detected upon careful review, potentially causing rescanning and workflow delays.
Similar Image Search for Histopathology: SMILY
SMILY may be a useful general-purpose tool in the pathologist's arsenal, to improve the efficiency of searching large archives of histopathology images, without the need to develop and implement specific tools for each application.
Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality
Three-dimensional (3-D) scene reconstruction is one of the key techniques in Augmented Reality (AR), which is related to the integration of image processing and display systems of complex information.
Detecting Anemia from Retinal Fundus Images
Despite its high prevalence, anemia is often undetected due to the invasiveness and cost of screening and diagnostic tests.
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric
Despite many previous works studying the reason behind such adversarial behavior, the relationship between the generalization performance and adversarial behavior of DNNs is still little understood.
Robust Regression via Deep Negative Correlation Learning
Nonlinear regression has been extensively employed in many computer vision problems (e. g., crowd counting, age estimation, affective computing).
An Evaluation of Feature Matchers for Fundamental Matrix Estimation
According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator.
A deep learning system for differential diagnosis of skin diseases
In this paper, we developed a deep learning system (DLS) to provide a differential diagnosis of skin conditions for clinical cases (skin photographs and associated medical histories).
AdaSample: Adaptive Sampling of Hard Positives for Descriptor Learning
The effectiveness of the triplet loss heavily relies on the triplet selection, in which a common practice is to first sample intra-class patches (positives) from the dataset for batch construction and then mine in-batch negatives to form triplets.
Deep learning-based survival prediction for multiple cancer types using histopathology images
Our analysis demonstrates the potential for this approach to provide prognostic information in multiple cancer types, and even within specific pathologic stages.
Ordered or Orderless: A Revisit for Video based Person Re-Identification
Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation.
Sub-Band Knowledge Distillation Framework for Speech Enhancement
In single-channel speech enhancement, methods based on full-band spectral features have been widely studied.
Rethinking Computer-Aided Tuberculosis Diagnosis
Computer-aided tuberculosis diagnosis (CTD) is a promising choice for TB diagnosis due to the great successes of deep learning.
Predicting Risk of Developing Diabetic Retinopathy using Deep Learning
We created and validated two versions of a deep learning system (DLS) to predict the development of mild-or-worse ("Mild+") DR in diabetic patients undergoing DR screening.
Deep Learning for Strong Lensing Search: Tests of the Convolutional Neural Networks and New Candidates from KiDS DR3
Using the latter training set, about 67\% of the aforementioned 48 candidates are also found, and there are 11 more new strong lensing candidates identified.
Cosmology and Nongalactic Astrophysics
DOTS: Decoupling Operation and Topology in Differentiable Architecture Search
DARTS mainly focuses on the operation search and derives the cell topology from the operation weights.
Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases
To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States.
Interpretable Survival Prediction for Colorectal Cancer using Deep Learning
Our approach can be used to explain predictions from a prognostic deep learning model and uncover potentially-novel prognostic features that can be reliably identified by people for future validation studies.
Detecting hidden signs of diabetes in external eye photographs
In validation set A (n=27, 415 patients, all undilated), the DLS detected poor blood glucose control (HbA1c > 9%) with an area under receiver operating characteristic curve (AUC) of 70. 2; moderate-or-worse DR with an AUC of 75. 3; diabetic macular edema with an AUC of 78. 0; and vision-threatening DR with an AUC of 79. 4.
Revealing Cluster Structures Based on Mixed Sampling Frequencies
This paper proposes a new linearized mixed data sampling (MIDAS) model and develops a framework to infer clusters in a panel regression with mixed frequency data.
Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading
's C-indices were 0. 87 and 0. 85 for continuous and discrete grading, respectively, compared to 0. 79 (95%CI 0. 71-0. 86) for GG obtained from the reports.
Coarse-grained decomposition and fine-grained interaction for multi-hop question answering
Recent advances regarding question answering and reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text, requiring only single-hop reasoning.
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions
We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions while detecting rare conditions for which there is not enough data available for training a confident classifier.
Deep learning for detecting pulmonary tuberculosis via chest radiography: an international study across 10 countries
Tuberculosis (TB) is a top-10 cause of death worldwide.
Rethinking Global Context in Crowd Counting
This paper investigates the role of global context for crowd counting.
Boosting Few-shot Semantic Segmentation with Transformers
Due to the fact that fully supervised semantic segmentation methods require sufficient fully-labeled data to work well and can not generalize to unseen classes, few-shot segmentation has attracted lots of research attention.
Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge of source domain to the unlabeled target domain.
Towards Disentangling Non-Robust and Robust Components in Performance Metric
Then, we show by experiments that DNNs under standard training rely heavily on optimizing the non-robust component in achieving decent performance.
On the Equivalence of Two Competing Affirmative Actions in School Choice
This note analyzes the outcome equivalence conditions of two popular affirmative action policies, majority quota and minority reserve, under the student optimal stable mechanism.
On the Asymptotic Performance of Affirmative Actions in School Choice
Given the possible preference manipulations under the IAM, we characterize the asymptotically equivalent sets of Nash equilibrium outcomes of the IAM with these two affirmative actions.
The PCG-AIID System for L3DAS22 Challenge: MIMO and MISO convolutional recurrent Network for Multi Channel Speech Enhancement and Speech Recognition
This paper described the PCG-AIID system for L3DAS22 challenge in Task 1: 3D speech enhancement in office reverberant environment.
Mutual Learning for Domain Adaptation: Self-distillation Image Dehazing Network with Sample-cycle
Specifically, we first devise two siamese networks: a teacher network in the synthetic domain and a student network in the real domain, and then optimize them in a mutual learning manner by leveraging EMA and joint loss.
Joint Noise Reduction and Listening Enhancement for Full-End Speech Enhancement
Speech enhancement (SE) methods mainly focus on recovering clean speech from noisy input.
Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule Diagnosis
However, detection and accurate diagnosis of pulmonary nodules depend heavily on the experiences of radiologists and can be a heavy workload for them.
Towards Efficient Single Image Dehazing and Desnowing
A single expert network efficiently addresses specific degradation in nasty winter scenes relying on the compact architecture and three novel components.
Towards Real-time High-Definition Image Snow Removal: Efficient Pyramid Network with Asymmetrical Encoder-decoder Architecture
In winter scenes, the degradation of images taken under snow can be pretty complex, where the spatial distribution of snowy degradation is varied from image to image.
MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing
Snow removal causes challenges due to its characteristic of complex degradations.
Discovering novel systemic biomarkers in photos of the external eye
On validation sets B and C, with substantial patient population differences compared to the development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by 7. 3-13. 2%.
Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes
Current efficient LiDAR-based detection frameworks are lacking in exploiting object relations, which naturally present in both spatial and temporal manners.
End-to-end Clinical Event Extraction from Chinese Electronic Health Record
Event extraction is an important work of medical text processing.
Dual-former: Hybrid Self-attention Transformer for Efficient Image Restoration
Recently, image restoration transformers have achieved comparable performance with previous state-of-the-art CNNs.
Deep Negative Correlation Classification
Ensemble learning serves as a straightforward way to improve the performance of almost any machine learning algorithm.
Large Language Models Encode Clinical Knowledge
To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars.
Ranked #4 on
Multiple Choice Question Answering (MCQA)
on MedMCQA
(Dev Set (Acc-%) metric)
RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement
Underwater images typically experience mixed degradations of brightness and structure caused by the absorption and scattering of light by suspended particles.
Generative Sentiment Transfer via Adaptive Masking
Sentiment transfer aims at revising the input text to satisfy a given sentiment polarity while retaining the original semantic content.
DEHRFormer: Real-time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes
Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges.
PointGame: Geometrically and Adaptively Masked Auto-Encoder on Point Clouds
Self-supervised learning is attracting large attention in point cloud understanding.
CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesis
In this work, we focus on a novel task of category-level functional hand-object manipulation synthesis covering both rigid and articulated object categories.
UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning
We propose a novel, object-agnostic method for learning a universal policy for dexterous object grasping from realistic point cloud observations and proprioceptive information under a table-top setting, namely UniDexGrasp++.
Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning
We compared the DLS with the office-based refit-WHO score, which adopts the shared predictors from WHO and Globorisk scores (age, sex, smoking status, height, weight and systolic blood pressure) but refitted on the UK Biobank (UKB) cohort.
Towards Expert-Level Medical Question Answering with Large Language Models
Large language models (LLMs) have catalyzed significant progress in medical question answering; Med-PaLM was the first model to exceed a "passing" score in US Medical Licensing Examination (USMLE) style questions with a score of 67. 2% on the MedQA dataset.
Using generative AI to investigate medical imagery models and datasets
In this paper, we present a method for automatic visual explanations leveraging team-based expertise by generating hypotheses of what visual signals in the images are correlated with the task.
Evaluating AI systems under uncertain ground truth: a case study in dermatology
In contrast, we propose a framework where aggregation is done using a statistical model.
$\mathrm{SAM^{Med}}$: A medical image annotation framework based on large vision model
The $\mathrm{SAM^{assist}}$ demonstrates the generalization ability of SAM to the downstream medical segmentation task using the prompt-learning approach.
Towards Generalist Biomedical AI
While considerable work is needed to validate these models in real-world use cases, our results represent a milestone towards the development of generalist biomedical AI systems.
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders
In this work, we present an approach, which we call Embeddings for Language/Image-aligned X-Rays, or ELIXR, that leverages a language-aligned image encoder combined or grafted onto a fixed LLM, PaLM 2, to perform a broad range of chest X-ray tasks.
Efficient Polyp Segmentation Via Integrity Learning
This paper introduces the integrity concept in polyp segmentation at both macro and micro levels, aiming to alleviate integrity deficiency.
Adverse Weather Removal with Codebook Priors
Inspired by recent advancements in codebook and vector quantization (VQ) techniques, we present a novel Adverse Weather Removal network with Codebook Priors (AWRCP) to address the problem of unified adverse weather removal.
Low-Resolution Self-Attention for Semantic Segmentation
Semantic segmentation tasks naturally require high-resolution information for pixel-wise segmentation and global context information for class prediction.
Domain-specific optimization and diverse evaluation of self-supervised models for histopathology
Foundation models in histopathology that learn general representations across a wide range of tissue types, diagnoses, and magnifications offer the potential to reduce the data, compute, and technical expertise necessary to develop task-specific deep learning models with the required level of model performance.
Towards Accurate Differential Diagnosis with Large Language Models
Comparing the two assisted study arms, the DDx quality score was higher for clinicians assisted by our LLM (top-10 accuracy 51. 7%) compared to clinicians without its assistance (36. 1%) (McNemar's Test: 45. 7, p < 0. 01) and clinicians with search (44. 4%) (4. 75, p = 0. 03).
TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding
Humans commonly work with multiple objects in daily life and can intuitively transfer manipulation skills to novel objects by understanding object functional regularities.
MINT: A wrapper to make multi-modal and multi-image AI models interactive
In this paper we tackle a more subtle challenge: doctors take a targeted medical history to obtain only the most pertinent pieces of information; how do we enable AI to do the same?
Closing the AI generalization gap by adjusting for dermatology condition distribution differences across clinical settings
Recently, there has been great progress in the ability of artificial intelligence (AI) algorithms to classify dermatological conditions from clinical photographs.
ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis Using Contrastive Learning
This framework is designed to introduce inherent transparency and provide extensive post-hoc explainability for deep learning model, making them more suitable for clinical medical diagnosis.
A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Large language models (LLMs) hold immense promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities.
Chasing Contests
This paper proposes a dynamic research contest, namely the chasing contest, in which two asymmetric contestants exert costly effort to accomplish two breakthroughs in a continuous-time framework.
