J0nasW/paperswithcode huggingface dataset:
Task: Causal Inference
Area: knowledge-base

Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

<span style="color:grey; opacity: 0.6">( Image credit: [Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data](https://arxiv.org/pdf/1605.00391v2.pdf) )</span>
Task: Symbolic Regression
Area: knowledge-base

producing a mathematical expression (symbolic expression)
that fits a given tabular data.
Task: Adversarial Attack Detection
Area: knowledge-base

The detection of adversarial attacks.
Task: Causal Discovery
Area: knowledge-base

<span style="color:grey; opacity: 0.6">( Image credit: [TCDF](https://github.com/M-Nauta/TCDF) )</span>
Task: Knowledge Base Completion
Area: knowledge-base

Knowledge base completion is the task which automatically infers missing facts by reasoning about the information already present in the knowledge base. A knowledge base is a collection of relational
facts, often represented in the form of "subject", "relation", "object"-triples.
Task: Sleep Stage Detection
Area: medical

Human Sleep Staging into W-N1-N2-N3-REM classes from multiple or single polysomnography signals
Task: Drug Discovery
Area: medical

Drug discovery is the task of applying machine learning to discover new candidate drugs.

<span style="color:grey; opacity: 0.6">( Image credit: [A Turing Test for Molecular Generators](https://pubs.acs.org/doi/10.1021/acs.jmedchem.0c01148) )</span>
Task: Medical waveform analysis
Area: medical

Information extraction from medical waveforms such as the electrocardiogram (ECG), arterial blood pressure (ABP) central venous pressure (CVP), photoplethysmogram (PPG, Pleth).
Task: Photoplethysmogram simulation
Area: medical

Simulating photoplethysmogram (PPG) signals
Task: Multimodal Sleep Stage Detection
Area: medical

Using multiple modalities such as EEG+EOG, EEG+HR instead of just relying on EEG (polysomnography)
Task: Mortality Prediction
Area: medical

<span style="color:grey; opacity: 0.6">( Image credit: [Early hospital mortality prediction using vital signals](https://arxiv.org/pdf/1803.06589v2.pdf) )</span>
Task: Domain/Motif Prediction
Area: medical

The domain prediction task which tasks language models with the identification of the domain type within a given protein sequence, which is defined as a compact folded three-dimensional structure.
Task: Sleep Quality
Area: medical

<span style="color:grey; opacity: 0.6">( Image credit: [DeepSleep](https://github.com/GuanLab/DeepSleep) )</span>
Task: 3D Medical Imaging Segmentation
Area: medical

3D medical imaging segmentation is the task of segmenting medical objects of interest from 3D medical imaging.

<span style="color:grey; opacity: 0.6">( Image credit: [Elastic Boundary Projection for 3D Medical Image Segmentation](https://github.com/twni2016/Elastic-Boundary-Projection) )</span>
Task: Photoplethysmography (PPG) heart rate estimation
Area: medical

Estimating heart rate from the photoplethysmogram (PPG) signal
Task: Photoplethysmography (PPG)
Area: medical

**Photoplethysmography (PPG)** is a non-invasive light-based method that has been used since the 1930s for monitoring cardiovascular activity.


<span class="description-source">Source: [Non-contact transmittance photoplethysmographic imaging (PPGI) for long-distance cardiovascular monitoring ](https://arxiv.org/abs/1503.06775)</span>
Task: Protein Design
Area: medical

Formally, given the design requirements of users, models are required to generate protein amino acid sequences that align with those requirements.
Task: Chemical Entity Recognition
Area: medical

Chemical Entity Recognition (CER) is a fundamental task in biomedical text mining and Natural Language Processing (NLP). It involves the identification and classification of chemical entities in textual data, such as scientific literature. These entities can encompass a broad range of concepts including chemical compounds, drugs, elements, ions or functional groups. Given the complexity and variety of chemical nomenclature, the CER task represents a significant challenge for LLMs, and their performance in this task can provide important insights into their overall capabilities in the biomedical domain.
Task: Forward reaction prediction
Area: medical

Forward reaction prediction pertains to the anticipatory determination of the probable product(s) of a chemical reaction, given specific reactants and reagents. 
This facilitates the optimization of research and development methodologies, curbs the extent of experimental speculation, and endorses greener chemistry practices by mitigating waste production.
Task: Heart Rate Variability
Area: medical

Heart rate variability (HRV) is the physiological phenomenon of variation in the time interval between heartbeats. It is measured by the variation in the beat-to-beat interval.
Task: COVID-19 Diagnosis
Area: medical

Covid-19 Diagnosis is the task of diagnosing the presence of COVID-19 in an individual with machine learning.
Task: Medical Procedure
Area: medical

Predicting medical procedures performed during a hospital admission.
Task: Chemical-Protein Interaction Extraction
Area: medical

The models are presented with excerpts from scientific literature and are required to not only identify distinct chemicals within the text but also to discern the specific nature of the interactions between them. This could involve, for instance, determining regulatory relationships between identified ligands and proteins.
Task: Diffeomorphic Medical Image Registration
Area: medical

Diffeomorphic mapping is the underlying technology for mapping and analyzing information measured in human anatomical coordinate systems which have been measured via Medical imaging. Diffeomorphic mapping is a broad term that actually refers to a number of different algorithms, processes, and methods. It is attached to many operations and has many applications for analysis and visualization. Diffeomorphic mapping can be used to relate various sources of information which are indexed as a function of spatial position as the key index variable. Diffeomorphisms are by their Latin root structure preserving transformations, which are in turn differentiable and therefore smooth, allowing for the calculation of metric based quantities such as arc length and surface areas. Spatial location and extents in human anatomical coordinate systems can be recorded via a variety of Medical imaging modalities, generally termed multi-modal medical imagery, providing either scalar and or vector quantities at each spatial location.


<span style="color:grey; opacity: 0.6">( Image credit: [Quicksilver](https://arxiv.org/pdf/1703.10908.pdf) )</span>
Task: Medical Report Generation
Area: medical

Medical report generation (MRG) is a task which focus on training AI to automatically generate professional report according the input image data. This can help clinicians make faster and more accurate decision since the task itself is both time consuming and error prone even for experienced doctors.



Deep neural network and transformer based architecture are currently the most popular methods for this certain task, however, when we try to transfer out pre-trained model into this certain domain, their performance always degrade. 



The following are some of the reasons why RSG is hard for pre-trained models:

* Language datasets in a particular domain can sometimes be quite different from the large number of datasets available on the Internet
* During the fine-tuning phase, datasets in the medical field are often unevenly distributed



More recently, multi-modal learning and contrastive learning have shown some inspiring results in this field, but it's still challenging and requires further attention.



Here are some additional readings to go deeper on the task:

* On the Automatic Generation of Medical Imaging Reports

  [ https://doi.org/10.48550/arXiv.1711.08195](https://doi.org/10.48550/arXiv.1711.08195)

* A scoping review of transfer learning research on medical image analysis using ImageNet 

  [ https://arxiv.org/abs/2004.13175](https://arxiv.org/abs/2004.13175)

* A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis

 [ https://arxiv.org/abs/2004.12150]( https://arxiv.org/abs/2004.12150)





(Image credit : Transformers in Medical Imaging: A Survey)
Task: Medical Relation Extraction
Area: medical

Biomedical relation extraction is the task of detecting and classifying semantic relationships from biomedical text.
Task: Brain Decoding
Area: medical

**Motor Brain Decoding** is fundamental task for building motor brain computer interfaces (BCI).

Progress in predicting finger movements based on brain activity allows us to restore motor functions and improve rehabilitation process of patients.
Task: Seizure Detection
Area: medical

**Seizure Detection** is a binary supervised classification problem with the aim of classifying between seizure and non-seizure states of a patient.


<span class="description-source">Source: [ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification ](https://arxiv.org/abs/2006.07900)</span>
Task: Catalytic activity prediction
Area: medical

The EC number, a numerical classification system for enzymes hinging on the chemical reactions they catalyze, is substituted with the corresponding reaction. This substitution aims to leverage the tacit knowledge ingrained in pre-trained language models, thereby encouraging the model to predict the reaction itself rather than the mere EC number.
Task: Surgical Skills Evaluation
Area: medical

The task is to classify surgical skills using data that is recorded during the surgical intervention.
Task: Remaining Length of Stay
Area: medical

Continuous prediction of the remaining ICU stay duration.
Task: Optic Cup Segmentation
Area: medical

Optic cup segmentation, concentric with optic disc, useful for glaucoma management (ophthalmology)
Task: Synthetic Data Generation
Area: medical

The generation of tabular data by any means possible.
Task: Single-cell modeling
Area: medical

Single Cell RNA sequencing (scRNAseq) revolutionized our understanding of the fundamental of life sciences. The technology enables an unprecedented resolution to study heterogeneity in cell populations and their functionalities.
Task: Radiologist Binary Classification
Area: medical

This task measures a radiologist's performance on distinguishing between generated (e.g. with a GAN, VAE, etc.)  and real images, ascribing to the high visual quality of the synthesized images, and to their potential use in advancing and facilitating downstream medical tasks.
Task: Respiratory motion forecasting
Area: medical

Respiratory motion forecasting to compensate for the latency of the radiotherapy treatment systems and target more accurately chest tumors.
Task: Brain Segmentation
Area: medical

<span style="color:grey; opacity: 0.6">( Image credit: [3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study](https://github.com/josedolz/LiviaNET) )</span>
Task: Heart rate estimation
Area: medical

RR interval detection and R peak detection from QRS complex
Task: Computational Phenotyping
Area: medical

**Computational Phenotyping** is the process of transforming the noisy, massive Electronic Health Record (EHR) data into meaningful medical concepts that can be used to predict the risk of disease for an individual, or the response to drug therapy.


<span class="description-source">Source: [Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis ](https://arxiv.org/abs/1908.09888)</span>
Task: Molecular Docking
Area: medical

Predicting the binding structure of a small molecule ligand to a protein, which is critical to drug design.

Description from: [DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking](https://paperswithcode.com/paper/diffdock-diffusion-steps-twists-and-turns-for)
Task: Retrosynthesis
Area: medical

Retrosynthetic analysis is a pivotal synthetic methodology in organic chemistry that employs a reverse-engineering approach, initiating from the target compound and retroactively tracing potential synthesis routes and precursor molecules. 
This technique proves instrumental in sculpting efficient synthetic strategies for intricate molecules, thus catalyzing the evolution and progression of novel pharmaceuticals and materials.
Task: ICU Mortality
Area: medical

Prediction of a patient mortality in the Intensive Care Unit (ICU) given its first hours of  Electronic Health Record (EHR).
Task: Medical Code Prediction
Area: medical

Context: Prediction of medical codes from clinical notes is both a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and excessive effort by human coders today. A new milestone will mark a meaningful step toward fully Autonomous Medical Coding in machines reaching parity with human coders' performance in medical code prediction.

Question: What exactly is the medical code prediction problem?

Answer: Clinical notes contain much information about what precisely happened during the patient's entire stay. And those clinical notes (e.g., discharge summary) is typically long, loosely structured, consists of medical domain language, and sometimes riddled with spelling errors. So, it's a highly multi-label classification problem, and the forthcoming ICD-11 standard will add more complexity to the problem! The medical code prediction problem is to annotate this clinical note with multiple codes subset from nearly 70K total codes (in the current ICD-10 system, for example).
Task: Functional Description Generation
Area: medical

The functional description generation task, which not only evaluates the reasoning capability of the language model in determining the function of a protein sequence but also assesses the efficacy of the language model's text generation.
Task: Medical Image Classification
Area: medical

**Medical Image Classification** is a task in medical image analysis that involves classifying medical images, such as X-rays, MRI scans, and CT scans, into different categories based on the type of image or the presence of specific structures or diseases. The goal is to use computer algorithms to automatically identify and classify medical images based on their content, which can help in diagnosis, treatment planning, and disease monitoring.
Task: Property Prediction
Area: medical

Property prediction involves forecasting or estimating a molecule's inherent physical and chemical properties based on information derived from its structural characteristics. 
It facilitates high-throughput evaluation of an extensive array of molecular properties, enabling the virtual screening of compounds. 
Additionally, it provides the means to predict the unknown attributes of new molecules, thereby bolstering research efficiency and reducing development times.
Task: Photoplethysmography (PPG) beat detection
Area: medical

Detecting heartbeats in the photoplethysmogram (PPG) signal
Task: Reagent Prediction
Area: medical

Reagent prediction endeavors to ascertain the suitable catalysts, solvents, or ancillary substances required for a specific chemical reaction.
This endeavor facilitates chemists in uncovering novel reaction types and mechanisms, identifying more optimal or eco-friendly reaction conditions, and ultimately streamlining the comprehensive chemical process to attain maximal cost-effectiveness and environmental stewardship.
Task: Medical Image Registration
Area: medical

Image registration, also known as image fusion or image matching, is the process of aligning two or more images based on image appearances. **Medical Image Registration** seeks to find an optimal spatial transformation that best aligns the underlying anatomical structures. Medical Image Registration is used in many clinical applications such as image guidance, motion tracking, segmentation, dose accumulation, image reconstruction and so on. Medical Image Registration is a broad topic which can be grouped from various perspectives. From input image point of view, registration methods can be divided into unimodal, multimodal, interpatient, intra-patient (e.g. same- or different-day) registration. From deformation model point of view, registration methods can be divided in to rigid, affine and deformable methods. From region of interest (ROI) perspective, registration methods can be grouped according to anatomical sites such as brain, lung registration and so on. From image pair dimension perspective, registration methods can be divided into 3D to 3D, 3D to 2D and 2D to 2D/3D.


<span class="description-source">Source: [Deep Learning in Medical Image Registration: A Review ](https://arxiv.org/abs/1912.12318)</span>
Task: Sleep Staging
Area: medical

Human Sleep Staging into W-R-N or W-R-L-D classes from multiple or single polysomnography signals
Task: Protein Function Prediction
Area: medical

For GO terms prediction, given the specific function prediction instruction and a protein sequence, models characterize the protein functions using the GO terms presented in three different domains (cellular component, biological process, and molecular function).
Task: Epidemiology
Area: medical

**Epidemiology** is a scientific discipline that provides reliable knowledge for clinical medicine focusing on prevention, diagnosis and treatment of diseases. Research in Epidemiology aims at characterizing risk factors for the outbreak of diseases and at evaluating the efficiency of certain treatment strategies, e.g., to compare a new treatment with an established gold standard. This research is strongly hypothesis-driven and statistical analysis is the major tool for epidemiologists so far. Correlations between genetic factors, environmental factors, life style-related parameters, age and diseases are analyzed.


<span class="description-source">Source: [Visual Analytics of Image-Centric Cohort Studies in Epidemiology ](https://arxiv.org/abs/1501.04009)</span>
Task: BIRL
Area: medical

BIRL: Benchmark on Image Registration methods with Landmark validation, in particular, Biomedical image registration on WSI microscopy images of a multi-strain histology tissue sample.
Task: Medical Image Segmentation
Area: medical

**Medical Image Segmentation** is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.

<span style="color:grey; opacity: 0.6">( Image credit: [IVD-Net](https://github.com/josedolz/IVD-Net) )</span>
Task: Chemical-Disease Interaction Extraction
Area: medical

The goal of this task is to discern the relationships between chemicals and diseases from given medical literature, a concept known as chemical-induced disease (CID) relations. These CID relations are paramount in biomedical research, playing crucial roles in areas such as drug discovery, toxicology, and disease etiology.
Task: Pancreas Segmentation
Area: medical

Pancreas segmentation is the task of segmenting out the pancreas from medical imaging.

Convolutional neural network
Task: Cell Segmentation
Area: medical

**Cell Segmentation** is a task of splitting a microscopic image domain into segments, which represent individual instances of cells. It is a fundamental step in many biomedical studies, and it is regarded as a cornerstone of image-based cellular research. Cellular morphology is an indicator of a physiological state of the cell, and a well-segmented image can capture biologically relevant morphological information.


<span class="description-source">Source: [Cell Segmentation by Combining Marker-controlled Watershed and Deep Learning ](https://arxiv.org/abs/2004.01607)</span>
Task: Participant Intervention Comparison Outcome Extraction
Area: medical

PICO recognition is an information extraction task for identifying Participant, Intervention, Comparator, and Outcome (PICO elements) information from clinical literature.
Task: Medical Diagnosis
Area: medical

**Medical Diagnosis** is the process of identifying the disease a patient is affected by, based on the assessment of specific risk factors, signs, symptoms and results of exams.


<span class="description-source">Source: [A probabilistic network for the diagnosis of acute cardiopulmonary diseases ](https://arxiv.org/abs/1609.06864)</span>
Task: Medical Image Generation
Area: medical

Medical image generation is the task of synthesising new medical images.

<span style="color:grey; opacity: 0.6">( Image credit: [Towards Adversarial Retinal Image Synthesis](https://arxiv.org/pdf/1701.08974v1.pdf) )</span>
Task: Eeg Decoding
Area: medical

**EEG Decoding** - extracting useful information directly from EEG data.
Task: Brain Tumor Segmentation
Area: medical

**Brain Tumor Segmentation** is a medical image analysis task that involves the separation of brain tumors from normal brain tissue in magnetic resonance imaging (MRI) scans. The goal of brain tumor segmentation is to produce a binary or multi-class segmentation map that accurately reflects the location and extent of the tumor.

<span style="color:grey; opacity: 0.6">( Image credit: [Brain Tumor Segmentation with Deep Neural Networks](https://github.com/naldeborgh7575/brain_segmentation) )</span>
Task: Genetic Risk Prediction
Area: medical

Polygenic Risk Scores (PRS) / Polygenic Scores (PGS)
Task: Clinical Concept Extraction
Area: medical

Automatic extraction of clinical named entities such as clinical problems, treatments, tests and anatomical parts from clinical notes.

( [Source](https://arxiv.org/pdf/2012.04005v1.pdf) )
Task: Retinal Vessel Segmentation
Area: medical

Retinal vessel segmentation is the task of segmenting vessels in retina imagery.

<span style="color:grey; opacity: 0.6">( Image credit: [LadderNet](https://github.com/juntang-zhuang/LadderNet) )</span>
Task: ECG Wave Delineation
Area: medical

Delineation of the waveforms P, T and QRS complexes from ECG signals
Task: Lesion Segmentation
Area: medical

Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.

<span style="color:grey; opacity: 0.6">( Image credit: [D-UNet](https://arxiv.org/pdf/1908.05104v1.pdf) )</span>
Task: Kidney Function
Area: medical

Continuous prediction of urine production in the next 2h as an average rate in ml/kg/h. The task is predicted at irregular intervals.
Task: Diabetic Retinopathy Grading
Area: medical

Grading the severity of diabetic retinopathy from (ophthalmic) fundus images
Task: Respiratory Failure
Area: medical

Continuous prediction of onset of respiratory failure in the next 12h given the patient is not in failure now.
Task: Circulatory Failure
Area: medical

Continuous prediction of onset of circulatory failure in the next 12h, given the patient is not in failure now.
Task: MHC presentation prediction
Area: medical

Task solves important immunological problem, which is predicting whether given peptide will present on given MHC.
Task: Patient Phenotyping
Area: medical

Classifying patients after 24h regarding their admission diagnosis,
using the APACHE group II and IV labels.
Task: Brain Morphometry
Area: medical

Measurement of brain structures from neuroimaging (MRI).
Task: Metal Artifact Reduction
Area: medical

Metal artifact reduction aims to remove the artifacts introduced by metallic implants in CT images.
Task: Sleep Arousal Detection
Area: medical

Sleep arousal is a kind of EEG events happened during octurnal sleep. Too many arousals will contribute to many health problem, like daytime sleepiness, memory loss, diabetes, etc. Some research take it as a kind of sleep deprivation.
Task: Sleep Quality Prediction
Area: medical

<span style="color:grey; opacity: 0.6">( Image credit: [DeepSleep](https://github.com/GuanLab/DeepSleep) )</span>